Julien Boelaert
email: julien.boelaert@gmail.com

Kudu Herd Optimization

This work proposes a new and simple algorithm for unconstrained numeric optimization over continuous spaces. A population of candidate solutions styled as a herd of kudus performs a succession of jumps through the search space in order to find the best solution (the kudu is a species of antelope). The logic of this algorithm is quite different from that of most populationbased algorithms, as the individual solutions are moved together in a herd-like fashion. Performance comparisons are conducted with the Artificial Bee Colony, Differential Evolution, the Genetic Algorithm and Particle Swarm Optimization on benchmark functions. The kudu herd seems to perform well in the early stages and on high-dimensional problems.

I. INTRODUCTION

Numerical optimization is an active research field with a wide range of applications. Ever since the emergence of evolutionary computation in the 1960s, much progress has been made in stochastic optimization techniques that make few assumptions about the function being optimized, in particular not requiring it to be continuous or differentiable. Exciting recent developments include Particle Swarm Optimization (PSO) [START_REF] Kennedy | Particle swarm optimization[END_REF], the Artificial Bee Colony algorithm (ABC) [START_REF] Karaboga | A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm[END_REF], both based on nature-inspired behaviour, and Differential Evolution (DE) [START_REF] Storn | Differential evolution -a simple and efficient heuristic for global optimization over continuous spaces[END_REF]. These evolutionary algorithms have proved to yield good results on benchmark and real-life problems.

The present contribution introduces a new algorithm named Kudu Herd Optimization. It can be thought of as mimicking the behaviour of a herd of kudus that jump their way through a solution space to find the optimal point. The kudu is a species of antelope present in sub-Saharan Africa, that mostly lives in herds and is known for its ability to jump high and far [START_REF] Burton | The international wildlife encyclopedia[END_REF]. The kudus were not the original inspiration for the algorithm, but provide a convenient and entertaining way of presenting its workings and logic. The algorithm tries to find an optimal point by iteratively changing a population of candidate solutions. However it does not rely on evolutionary principles or swarm intelligence, but rather on herd-like behaviour with centralized decisions. Results indicate that it performs well on a number of classic benchmark problems taken from the literature.

The rest of this paper is organized as follows. Section 2 describes the KHO algorithm, first through an illustrative story, then more formally. Section 3 presents the experimental protocol and the results of performance comparison between KHO, ABC, DE, Genetic Algorithm (GA), and PSO. Section 4 contains some concluding comments.

II. DESCRIPTION OF THE ALGORITHM

A. Illustrative story

The logic of KHO is best understood through a story. Let us state first that the behaviour of the kudu herd we describe is purely fictional -apart from the fact that they are often found in herds and that they are good jumpers, we claim no knowledge of kudu behaviour. Let us then imagine a herd of kudus jumping around their habitat in search of the location containing the best food -a kudu finding itself in any point in space can give it a real-valued mark, indicating food quality. Most kudus in our herd are fairly unimaginative, and they all follow one intelligent leader-kudu in their jumps. The herd moves in the following way:

• The leader jumps to a given location, and all the other kudus jump to random positions around the leader. • The kudus then report their new positions and the corresponding quality to the leader. Based on this information, the leader determines the direction of its next jump (by computing the covariance of a function of picnic-quality with the position coordinates). • The leader's jump distance grows smaller if two consecutive jumps are in opposite directions, otherwise it grows larger. • Several jumps are performed in this fashion, and the size of the scatter region around the leader can be reduced over time to exploit information from narrower regions. • The herd remembers the single best location it has found so far.

B. Formal description and pseudo-code

Formally, we try to find the point that minimizes a realvalued cost function over a given bounded D-dimensional realvalued search space:

arg min x cost(x), x ∈ S S = [lb 1 , ub 1 ] × . . . × [lb D , ub D ]
For this purpose we use a population of P kudus (Ddimensional vectors representing candidate solutions), of which one is the leader. The algorithm iterates over four steps: place the leader in the search space, randomly place the other kudus in an area around the leader, evaluate the cost function for each kudu, and use these evaluations to compute the leader's next position.

Introducting some notations, let:

• ----→ leader be an D-dimensional vector containing the leader's position, • A be a P ×D matrix whose first row is ----→ leader and whose remaining rows contain the other kudus' positions,

• ---→ rank be a P -dimensional vector containing the costranking of each of the kudus,

• ---→ jump be an D-dimensional vector giving the direction of the leader's jump,

• jlength be a scalar value representing the leader's jump's length, • scatter be a scalar value controlling how close to the leader the kudus will be scattered, • η + and η -be scalar values used to automatically lengthen or shorten the jumps, • mlength be a scalar value representing the minimal allowed jump length. Then in pseudo-code the KHO algorithm operates as follows:

1) Initialization a) Initialize the leader to a random point in the search space. b) Initialize the (P -1) other kudus to random points in a region around the leader, with parameter scatter controlling the size of this region. c) Evaluate the cost function at each of the P points. d) Rank the costs and store the result in ---→ rank. e) Store the lowest cost and the associated position. f) Compute the jump direction according to the following formula:

∀i ∈ [1, D], ---→ jump i = cov( ---→ rank, A .i ) (1)
where ---→ jump i denotes the i-th element of the jump vector and A .i denotes the i-th column of matrix A. g) Initialize jump-length variable jlength to the maximal distance between the leader and the other kudus. 2) Loop (until termination criterion is met) a) Update the leader's position according to the following formula:

------→ leader t+1 = ----→ leader t - jlength t ----→ jump t ----→ jump t b)

C. Parameters and taxonomy

The algorithm essentially takes 3 parameters: the population size P , the scatter-range scatter, and the minimal jump-length mlength. Their role is easily understood: P is the number of cost-function evaluations at each iteration, which should affect the quality of jump direction (more evaluations at each iteration means a better "understanding" of the topology of the cost-surface around the leader) and the intensity of exploitation of each visited area (more evaluations give a better probability to pick a good point in the local search-area). The scatterrange controls "how local" the search is at each iteration: a lower scatter value will let the random evaluations occur in a narrower region around the leader. The minimum jump-length is used to avoid convergence to local optima. In section 3 we try to apprehend the influence of P , scatter and number of iterations on performance.

Parameters η + and η -are directly inspired by those of Riedmiller and Braun's RPROP algorithm [START_REF] Riedmiller | A direct adaptive method for faster backpropagation learning: the RPROP algorithm[END_REF] for the training of feedforward neural networks. The idea, paraphrasing the original article, is that two consecutive jumps in opposite directions (which in KHO is detected by a negative dot product) indicate that the last jump was too long and the algorithm has jumped over a local minimum; jump-length is then decreased by factor η -. Otherwise, jump-length is slightly increased (by factor η + ) in order to accelerate convergence in shallow regions. Although our setting is quite different, we used the original values of both parameters, η -= 0.5 and η + = 1.2.

No attempt has been made as yet to assess their influence on performance.

We chose to use the rank of costs instead of costs to compute the jump direction. This makes the algorithm invariant to any increasing transformation of the cost function. Preliminary results seem to indicate that using ranks instead of cost does not significantly affect performance on benchmark functions. Other choices are of course possible, for instance using logrank, exp-rank or squared rank to give more importance to lower or higher costs, and this choice might affect performance. Another discussable choice concerns the random placements around the leader. For the experiments of section III the kudus were uniformly distributed inside a hyperparallelepiped centered on the leader, according to the following formula:

∀(i, j)∈[2, P ] × [1, D], a ij = ----→ leader j + (r ij -0.5) × scatter × (ub j -lb j )
where a ij denotes element (i, j) of matrix A, ----→ leader j is the leader-vector's j-th element, scatter is a scalar value chosen

F 1 Ackley 20 + e -20 exp(-0.2 1 D D i=1 x 2 i ) - exp( 1 D D i=1 cos(2πx i )) [-32, 32] D N Y Y F 2 Bohachevsky D-1 i=1 (x 2 i + 2x 2 i+1 -0.3 cos(3πx i ) - 0.4 cos(4πx i+1 ) + 0.7) [-15, 15] D Y N N F 3 Griewank D i=1 x 2 i 4000 -D i=1 cos( x i √ i ) + 1 [-600, 600] D N N N F 4 Rastrigin D i=1 (x 2 i -10 cos(2πx i ) + 10) [-5, 5] D N Y Y F 5 Rosenbrock D-1 i=1 (100(x 2 i -x i+1 ) 2 + (x i -1) 2 ) [-100, 100] D N N Y F 6 Schaffer D-1 i=1 (x 2 i + x 2 i+1 ) 0.25 (sin 2 (50(x 2 i + x 2 i+1 ) 0.1 ) + 1) [-100, 100] D Y N Y F 7 Schwefel 1.2 D i=1 ( i j=1 x j ) 2 [-65.536, 65.536] D Y N N F 8 Schwefel 2.21 max i {|x i |, 1 ≤ i ≤ D} [-100, 100] D Y N N F 9 Schwefel 2.22 D i=1 |x i | + D i=1 |x i | [-10, 10] D Y Y Y F 10 Sphere D i=1 x 2 i [-100, 100] D Y Y Y
in [0, 1], ub j and lb j are respectively the upper and lower bounds of dimension j, and r ij is a random value uniformly drawn from [0, 1]. A better choice might be to generate normal deviates from ----→ leader. The KHO algorithm uses a population of candidate solutions that it changes and evaluates at each iteration. This property, which is shared by population-based algorithms such as ABC, DE, GA and PSO, makes parallelization of these algorithms easy, a very desirable property when evaluations are timeconsuming. Whether KHO can be called a population-based algorithm is, however, discussable. In KHO the individual solutions that make up the population have no autonomy, as all main decisions are centralized (jump direction and length are computed by the leader using the information provided by the whole population). At each iteration these individuals are randomly placed around the leader, so that contrary to the algorithms cited above different individuals never simultaneously explore wildly different regions. Furthermore their new coordinates do not depend on previous individual coordinates. This lack of autonomy sets KHO apart from most populationbased algorithms, as its population moves through the solutionspace in a herd-like fashion. This and the lack of inheritance in the transformation process also means that KHO does not belong to the family of evolutionary algorithms, and does not make use of swarm intelligence (as defined in [START_REF] Kennedy | Swarm Intelligence Morgan Kaufmann[END_REF]).

III. EXPERIMENT AND RESULTS

A. Experimental design

In order to test the performance of the KHO algorithm we use the unshifted expression of 10 classic benchmark functions as specified in [START_REF] Herrera | Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems[END_REF]. As can be seen in table I their expressions and properties differ, but they are all scalable to any number D of dimensions, and all offer at least one global minimum of value 0.

Performance was compared to that of ABC, DE, GA, PSO and a random search. Comparing optimization algorithms in a rigorous way is a notoriously difficult and time-consuming exercise, but we have tried to make the results as objective and reproducible as possible. Each algorithm was tried with varying population size (20, 50, 100, 200) and number of iterations (50, 100, 200, 1000), and each cost function was tried with varying number of dimensions [START_REF] Kennedy | Swarm Intelligence Morgan Kaufmann[END_REF]20,50,100,200). Each combination was run 100 times. The algorithms are compared through cost medians and standard deviations, on a basis of same problem dimension, population size and number of iterations. Lack of space forbids the presentation of all the detailed results, but a summary is given in subsection III-C.

B. Algorithms and settings

All trials were run using R version 2.13.1. Following is a brief description of the algorithms and settings used.

1) Artificial Bee Colony: The ABC algorithm was proposed by Karaboga in 2007 [START_REF] Karaboga | A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm[END_REF] as an optimization technique inspired by the foraging behaviour of honey bees. Candidate solutions are called food sources. The population is divided into employed bees and onlooker bees. In each iteration each employed bee tries to find one better food source than its previous one, applying a greedy selection if it succeds. Each onlooker bee is then directed towards one of the existing food sources, with a probability depending on the fitness of these sources, and tries to improve it, again applying a greedy selection. Each food source can only be exploited a given number of times, after which it is abandoned and replaced by a new random source.

The algorithm was implemented by plugging the C code found on the official ABC website [START_REF]Artificial Bee Colony Algorithm Homepage[END_REF] into the Rcpp package [START_REF] Eddelbuettel | Rcpp: Seamless R and C++ Integration[END_REF]. The limit parameter, controlling the number of maximal exploitations per food source, was set to 100 for all runs.

2) Differential Evolution: The DE algorithm, introduced by Storn and Price in 1997 [START_REF] Storn | Differential evolution -a simple and efficient heuristic for global optimization over continuous spaces[END_REF], is an evolutionary algorithm in which the evolution of individual agents is based on sums and differences between coordinates of existing agents. Its most simple expression is the following: at each iteration, for each member of the population x, three existing agents a,b and c are chosen at random, and a new candidate solution is created by crossover between x and a + F × (b -c), using a crossover probability CR and ensuring that at least one coordinate of x is conserved in the new candidate. A greedy selection is then applied between x and the new candidate solution.

The tests were run using the R package DEoptim version 2.1-2 [START_REF] Mullen | DEoptim': An R Package for Global Optimization by Differential Evolution[END_REF]. We used the classical DE/rand/1/bin strategy, and the package's standard values for F and CR, respectively set to 0.8 and 0.5.

3) Genetic Algorithm: GA has a long history of usage in many different settings since its introduction by Holland in 1975 [START_REF] Holland | Adaptation in Natural and Artificial Systems[END_REF]. The basic principle is to evolve children solutions from a population of parent solutions by the use of selection, crossover and mutation operators.

We used the real-vector version of GA implemented in function rbga of R package genalg version 0.1.1 [START_REF] Willighagen | genalg: R Based Genetic Algorithm[END_REF]. Except for the population size and number of iterations, we used the package's standard parameters: mutation probability of 1/(D + 1) where D is the number of dimensions, and elitism of about 20% of the population size.

4) Particle Swarm Optimization: PSO was introduced by Kennedy and Eberhart in 1995 [START_REF] Kennedy | Particle swarm optimization[END_REF] and is inspired by the flocking behaviour of birds and fish. A population of candidate solutions called particles is flown around the search space in the following way. At each iteration, each particle's position is changed by adding to it a random velocity vector which is a function of the previous velocity, of the difference vector between the particle's present position and its remembered best position, and of the difference vector between the particle's present position and the remembered best position of a set of informant particles.

We used the psoptim function provided by R package pso [START_REF] Bendtsen | pso: Particle Swarm Optimization" R package version 1[END_REF], which follows the "standard pso 2007" specification by Clerc et al. [START_REF] Clerc | Standard particle swarm optimisation 2007[END_REF]. Except for the population size and number of iterations, the default settings were used: exploitation constant of 1/(2 log(2)), local and global exploitation constants both set to 0.5 + log(2), random order of particle processing, no restarting, no clamping of velocity, and average percent of informants for each particle of 1 -(1 -1/popsize) 3 .

5) Random Search: In order to detect bad performance we included a random search algorithm, which is simply the best solution found among iter × popsize random guesses uniformly distributed over the search space.

6) KHO: The algorithm was run as described in section II-B, with a minimal jump length of 1e -20. It should be noted that for a same population size and number of iterations the KHO algorithm performs more operations than the other algorihtms, as for each iteration it has to compute covariances of the solution ranks with the population's coordinates.

The algorithm was coded in C++ and integrated in the R environment using the Rcpp package. Five different scatter parameters were tried: in four implementations the parameter is constant over all the iterations (0.3, 0.1, 0.01, 0.001) and in the fifth implementation the parameter is linearly decreased from 0.1 to 0.001 over the iterations.

C. Results

Tables II through V show some of the results obtained on the benchmark functions for 10 and 200 dimensions. These tables reflect the main results of the experiment: KHO finds a relatively good solution very fast (as can be seen in tables II and IV it often gets much better results than the other algorithms after 50 iterations), but seems unable to make significant progress in subsequent iterations. After many iterations (typically 200 in low dimensions and 1000 in high dimensions) the other algorithms are able to catch up with it and attain better solutions. This double result is observed in nearly all settings for functions F 1 , F 2 , F 3 , F 5 , F 6 , F 8 and F 10 . It is unclear whether KHO gets stuck in local optima or whether it is unable to exploit the good regions it has found to the fullest. The example of function F 10 , which is unimodal and has no local "traps", and on which, in low dimensions, KHO is bested by PSO and ABC for 200 iterations or more, indicates that good exploitation is indeed an issue.

The results clearly indicate that the scatter parameter plays a crucial role in KHO's performance, and that the optimal value depends on the considered problem. An extreme example of this is function F 1 , on which KHO performs well when the parameter is set to 0.1 or linearly decreased from 0.1 to 0.001, but gives very bad results -equivalent to or worse than those of a random search -when it is set to 0.01 or 0.001. On the other hand, on F 10 the best parameter is always the lowest, 0.001. Letting the parameter decrease with each iteration seems to be a good choice in many settings. An auto-adaptive scheme for this parameter might give good results.

KHO has relatively bad results on functions F 7 and F 9 . Interestingly, on function F 4 KHO obtains very bad results in 10 to 50 dimensions (similar to random search or worse), but gets much better in 100 and 200 dimensions (again finding good solutions comparatively fast, but being bested after many iterations). More generally, the algorithm performs comparatively better in high dimensions: it still finds good solutions after only a small number of iterations, and keeps its relative advantage to other algorithms much longer than in lower dimensions (most algorithms struggle with high dimensions and require a great number of iterations to attain good solutions).

IV. CONCLUSION

Kudu herd optimization is a simple algorithm that seems to perform well on many difficult problems. Performance comparisons with well-known population-based algorithms give encouraging results: KHO seems to find good solutions faster than other agorithms, and to respond comparatively well to the problem of scalability. It seems, however, to suffer from a lack of exploiting behaviour when run with a large number of iterations.

Future research could improve the algorithm in many directions. We suspect that using normal deviations instead

Table I BENCHMARK

 I FUNCTIONS F1-F10 DEFINITION, RANGE AND CHARACTERISTICS

	Function Name	Definition	Range	Unimodal Separable Easily optimized
				dimension by
				dimension

Table II MEDIAN

 II COST OBTAINED BY ABC, DE, GA, PSO, KHO AND RANDOM SEARCH ON THE TEST SUITE, 100 TRIALS (FIVE DIFFERENT CHOICES FOR KHO'S SCATTER PARAMETER : FIXED AT 0.3, 0.1, 0.01, AND 0.001, AND LINEARLY DECREASED FROM 0.1 TO 0.001) DIM=10, POPSIZE= 50, ITER= 50 (STANDARD DEVIATIONS IN PARENTHESES, BEST MEDIAN IN BOLDFACE).

		f1	f2	f3	f4	f5
	ABC	5.042 (1.645) 1.712 (1.133) 0.9627 (0.2855) 10.76 (3.433)	1172 ( 6766)
	DE	8.543 (0.8296) 23.58 (5.953)	3.54 (0.9653) 29.01 (4.866) 1.577e+06 (1.274e+06)
	GA	10.31 (1.359) 48.52 (17.22)	7.829 ( 2.98)	23.52 (5.906) 1.422e+07 (2.02e+07)
	PSO	3.639 (0.4723) 6.328 (1.156)	1.239 (0.1309) 40.54 (7.666) 2.213e+04 (2.443e+04)
	Random	17.76 (1.042) 380.9 (83.04)	59.07 (12.65)	78.2 (9.346) 5.822e+08 (2.918e+08)
	KHO 0.3	2.674 (0.5356) 4.903 (1.698)	1.074 (0.06662) 35.46 (10.16) 1.859e+05 (4.627e+05)
	KHO 0.1	1.016 (5.283) 1.217 (0.562)	0.987 (0.07092) 84.21 ( 26.1) 1.033e+05 (2.977e+05)
	KHO 0.01	19.58 (0.497) 5.209 (1.526) 0.08589 (0.06632) 84.94 (25.36)	868.6 (7.693e+04)
	KHO 0.001	19.53 (0.3751) 6.136 (1.762) 0.07859 (0.293)	91.04 (23.19)	3887 (1.311e+05)
	KHO 0.1-0.001	0.2189 (7.238) 0.2944 (0.2159) 0.4287 (0.1646) 78.84 (24.58)	6134 (1.895e+05)
		f6	f7	f8	f9	f10
	ABC	10.16 (1.415)	1177 (492.9)	28.33 (7.541) 0.3464 (0.2068)	0.819 (5.719)
	DE	12.56 (1.384)	1256 (378.7)	24.75 (4.241)	3.745 (0.6137)	309.2 (110.4)
	GA	11.87 ( 2.02) 827.6 (371.8)	24.77 ( 5.08)	4.827 (1.224)	753.9 (396.3)
	PSO	14.83 (2.538) 171.9 (104.3)	5.09 (1.158)	1.366 (0.3425)	24.99 (12.02)
	Random	(3.386)	2739 (657.9)	43.4 (5.171)	22.95 (3.975)	6463 ( 1473)
	KHO 0.3	16.15 (4.581)	1559 (957.2) 0.9454 (0.3826)	8.75 (7.622)	8.853 (5.141)
	KHO 0.1	19.6 ( 4.64)	388 (271.3) 0.3334 (0.5184) 29.59 (24.23)	1.015 (0.6104)
	KHO 0.01	14.5 ( 5.1)	493 ( 1025)	45.42 (14.08)	63.53 (229.7)	0.01107 (0.006097)
	KHO 0.001	14.44 ( 10.8) 956.4 ( 9190)	73.32 (12.44)	51.15 (28.71) 0.0002638 (0.001495)
	KHO 0.1-0.001	14.98 (4.579) 236.2 (462.2) 0.08873 (5.924)	32.74 (17.65)	0.08066 (0.08757)

Table III MEDIAN

 III COST OBTAINED BY ABC, DE, GA, PSO, KHO AND RANDOM SEARCH ON THE TEST SUITE, 100 TRIALS (FIVE DIFFERENT CHOICES FOR KHO'S SCATTER PARAMETER : FIXED AT 0.3, 0.1, 0.01, AND 0.001, AND LINEARLY DECREASED FROM 0.1 TO 0.001) DIM=10, POPSIZE= 50, ITER= 1000 (STANDARD DEVIATIONS IN PARENTHESES, BEST MEDIAN IN BOLDFACE).

		f1	f2	f3	f4	f5
	ABC	7.105e-15 (2.672e-15)	0 ( 0)	6.874e-08 (0.006576)	0 ( 0)	0.8951 (2.486)
	DE	7.416e-14 (3.253e-14)	0 ( 0)	1.127e-08 (1.331e-05)	0 ( 0)	0.5597 (0.6756)
	GA	0.405 (0.4804)	0.2636 (0.4649)	0.2788 (0.1285)	0.06823 (0.2336)	309.8 (560.2)
	PSO	3.109e-15 ( 0)	0 ( 0)	0.02987 (0.02224)	2.985 ( 1.95)	0.8642 (8.623)
	Random	15.36 (1.047)	206.5 (49.24)	33.45 (6.986)	60.85 (6.156)	1.502e+08 (7.715e+07)
	KHO 0.3	2.755 (0.5298)	4.997 (1.529)	1.077 (0.07309)	25.42 (6.288)	1.262e+05 (3.999e+05)
	KHO 0.1	0.9013 (4.576)	1.153 (0.5725)	0.9808 (0.08427)	82.34 (28.73)	3.638e+04 (1.986e+05)
	KHO 0.01	19.54 (0.3989)	4.828 (1.667)	0.06546 (0.04697)	86.67 (24.38)	156.1 ( 2742)
	KHO 0.001	19.47 (0.4189)	5.781 ( 1.45)	0.03901 (0.05073)	84.57 ( 22.1)	9.54 (6.669e+07)
	KHO 0.1-0.001	0.1539 (5.095)	0.3468 (0.2488)	0.09367 (0.1925)	78.67 (24.82)	322.4 (1.712e+05)
		f6	f7	f8	f9	f10
	ABC	2.659 (0.7143)	10.94 (12.08)	0.2239 (0.1311)	3.246e-16 (9.051e-17) 9.679e-17 (3.789e-17)
	DE	3.572 (0.5385)	0.1085 (0.07433) 0.0002765 (0.0001027) 2.29e-15 (1.079e-15) 1.287e-26 (1.796e-26)
	GA	4.604 (1.579)	8.19 (7.973)	1.813 (0.4921)	0.07912 (0.03888)	0.1653 (0.3744)
	PSO	5.491 (1.258)	2.014e-14 (4.147e-13) 1.208e-15 (1.34e-15) 1.286e-21 (2.208e-21) 4.933e-41 (1.806e-40)
	Random	16.42 (1.715)	1563 (318.9)	32.29 (4.444)	15.47 (2.288)	3414 (822.9)
	KHO 0.3	12.52 (3.498)	662.9 (882.3)	0.8498 (0.3285)	8.441 (7.556)	8.929 ( 5.28)
	KHO 0.1	16.21 (5.381)	145.6 ( 78.9)	0.2931 (0.1077)	21.72 (15.31)	0.9661 (0.5751)
	KHO 0.01	13.45 (3.956)	2.344 ( 6085)	0.02826 (0.01003)	48.29 (35.19)	0.009647 (0.006323)
	KHO 0.001	12.76 (8.224)	0.02768 (1.761e+04)	23.98 (11.83)	47.81 (10.38)	9.704e-05 (5.153e-05)
	KHO 0.1-0.001	13.12 (5.149)	1.089 (2.798)	0.04958 (0.04955)	0.07108 (17.41)	0.006017 (0.04688)
	of the uniform scattering might improve convergence and
	exploitation. The role of parameters η + and η -, and of the
	score-ranking scheme should be investigated. Auto-adaptive
	schemes for the adjustment of crucial parameter scatter might
	enhance performance on many problems. The algorithm could
	be adapted relatively easily to problems of constrained opti-
	mization. Finally, the results seem to indicate a certain com-
	plementarity of KHO with other population-based algorithms,
	which should make it a good candidate for hybridization.

Table IV MEDIAN

 IV COST OBTAINED BY ABC, DE, GA, PSO, KHO AND RANDOM SEARCH ON THE TEST SUITE, 100 TRIALS (FIVE DIFFERENT CHOICES FOR KHO'S SCATTER PARAMETER : FIXED AT 0.3, 0.1, 0.01, AND 0.001, AND LINEARLY DECREASED FROM 0.1 TO 0.001) DIM=200, POPSIZE= 200, ITER= 50 (STANDARD DEVIATIONS IN PARENTHESES, BEST MEDIAN IN BOLDFACE).

		f1	f2	f3	f4	f5
	ABC	20.73 (0.04959) 3.237e+04 (982.5)	4292 (146.9)	2859 (74.64)	2.467e+11 (1.394e+10)
	DE	20.71 (0.04798) 3.058e+04 ( 1026)	4097 ( 120)	2943 (49.89)	2.246e+11 (1.082e+10)
	GA	19.96 (0.1113) 1.953e+04 (883.3)	2644 (126.4)	2268 (56.54)	1.053e+11 (8.466e+09)
	PSO	15.3 (0.3637)	4311 (386.9)	561.9 ( 49.5)	2070 (47.98)	6.91e+09 (1.391e+09)
	Random	20.88 (0.02924) 3.455e+04 (716.5)	4602 (110.6)	3124 (44.72)	2.643e+11 (8.832e+09)
	KHO 0.3	11.05 (4.591)	183.8 (697.7)	6.467 ( 87.2)	1786 (177.3)	4.904e+05 (5.947e+05)
	KHO 0.1	9.545 (4.205)	64.98 (308.5)	1.648 (44.18)	2015 (207.4)	1.747e+05 (3.289e+05)
	KHO 0.01	19.58 (0.07194)	114.5 (9.739)	0.4737 (0.1034)	1721 (119.5)	1.326e+04 (1.684e+07)
	KHO 0.001	19.5 (0.06086)	145.6 (13.54)	0.9237 (0.1494)	1666 (117.2)	6.002e+04 (5.411e+09)
	KHO 0.1-0.001	7.961 (7.598)	19.71 (201.5)	0.7366 (32.49)	1577 (99.78)	1.367e+04 (3.207e+05)
		f6	f7	f8	f9	f10
	ABC	1160 (44.43) 6.197e+05 (9.622e+04) 97.01 (0.6387) 6.556e+31 (4.402e+46) 4.805e+05 (1.703e+04)
	DE	1240 ( 28.7)	7.196e+05 (8.821e+04) 96.17 (0.8543) 1.95e+79 (3.91e+83) 4.568e+05 (1.493e+04)
	GA	912.6 (30.42) 3.654e+05 (4.737e+04) 90.65 ( 1.38) 3.762e+35 (3.526e+44) 2.93e+05 (1.533e+04)
	PSO	942.8 (35.79) 3.839e+05 (6.097e+04) 66.17 (4.024)	308.2 ( 340)	6.288e+04 ( 5492)
	Random	1356 ( 25.8)	8.975e+05 (1.196e+05) 95.3 (0.6393) 5.436e+87 (3.848e+90) 5.117e+05 (1.228e+04)
	KHO 0.3	1144 (101.1) 7.291e+05 (1.674e+05) 93.21 (7.536)	486.8 ( 66.4)	649.3 (1.08e+04)
	KHO 0.1	966.8 (98.15) 5.673e+05 (1.183e+05) 78.97 (3.307)	755.9 (1.826e+29)	73.04 ( 3759)
	KHO 0.01	470.7 (53.11) 6.567e+05 (3.093e+05) 89.88 (1.741) 1.888e+87 (5.761e+107)	1.186 (0.2198)
	KHO 0.001	818.8 (258.1) 3.044e+06 (4.718e+06) 95.2 (1.189) 3.373e+105 (5.354e+119)	0.8473 (0.8737)
	KHO 0.1-0.001	474.7 (96.66) 5.083e+05 (1.144e+05) 80.08 (3.064)	811.3 (8.129e+16)	2.354 ( 2196)

Table V MEDIAN

 V COST OBTAINED BY ABC, DE, GA, PSO, KHO AND RANDOM SEARCH ON THE TEST SUITE, 100 TRIALS (FIVE DIFFERENT CHOICES FOR KHO'S SCATTER PARAMETER : FIXED AT 0.3, 0.1, 0.01, AND 0.001, AND LINEARLY DECREASED FROM 0.1 TO 0.001) DIM=200, POPSIZE= 200, ITER= 1000 (STANDARD DEVIATIONS IN PARENTHESES, BEST MEDIAN IN BOLDFACE).

		f1	f2	f3	f4	f5
	ABC	10.03 (0.6114)	107.8 (78.39)	5.939 (6.538)	397.9 (24.85)	2.341e+05 (6.826e+06)
	DE	11.78 (0.1911)	1304 (75.26)	158.3 (8.036)	978.1 (23.85)	7.215e+08 (9.416e+07)
	GA	8.981 (0.2693)	813.6 (57.45)	92.1 (8.218)	348.8 (19.64)	2.618e+08 (4.978e+07)
	PSO	2.818 (0.3042)	73.87 (7.768)	1.522 (0.1487)	497.1 (192.3)	9.009e+05 (4.291e+05)
	Random	20.8 (0.02844) 3.271e+04 (672.2)	4351 ( 93)	3027 (38.65)	2.424e+11 (7.862e+09)
	KHO 0.3	11 (4.709)	174.3 ( 750)	6.098 (95.79)	1464 ( 190)	3.511e+05 (5.297e+05)
	KHO 0.1	9.102 (4.457)	50.13 (266.9)	1.464 ( 30.6)	1991 (185.6)	2.27e+05 (4.271e+05)
	KHO 0.01	19.56 (0.07455)	112.2 (10.32)	0.2245 (0.08447)	1695 (103.6)	1158 (2.821e+08)
	KHO 0.001	19.5 (0.05831)	130.6 (8.636)	0.1065 (0.03238)	1685 (101.5)	302.3 (1.684e+09)
	KHO 0.1-0.001	7.293 (3.932)	20.6 ( 221)	0.06891 (27.87)	1572 (115.3)	306.6 (3.013e+05)
		f6	f7	f8	f9	f10
	ABC	317 (10.32) 2.666e+05 (1.785e+04) 94.97 (0.7385)	2.231 (0.4175)	526.4 (770.1)
	DE	446.3 ( 9.36) 2.888e+05 (2.226e+04) 85.73 (1.305)	129.1 (3.543)	1.753e+04 (992.8)
	GA	271.4 (10.79)	9.43e+04 ( 7620)	55.23 (2.548)	76.5 (4.063)	9815 (793.9)
	PSO	520.3 (92.83) 1.139e+05 (2.101e+04) 29.78 (2.105)	4.263 (1.028)	57.61 (16.55)
	Random	1302 (23.28) 6.701e+05 (6.33e+04)


Stefan Boeschen
email: stefan.boeschen@kit.edu

Martine Legris
email: mart.revel@gmail.com

ST 21 "The scientific activity in the prism of the participative imperative"

. It is shown that these involvements are attractive for civil society actors due to the chance of research agenda setting, and for researchers due to the access to specific knowledge resources and human body materials. In other fields, like nanotechnology, there are also attempts for participation but the goals are quite different. They are mainly defined by the precautionary principle to avoid risk problems (e.g. Kaiser et al. 2010). Moreover, participation was generalized in innovation theory (Baldwin/Hippel 2011). In sum: participation of civil society actors is seen as a main resource for improving processes of research and innovation.

In the meantime, the quest for participation is also integrated in the policy programs of the European Commission. Since the main goal of the Lisbon Strategy of the EU was to create a high competitive area of a knowledge-based economy, the change in the overarching innovation-regime by integrating different forms of knowledge and actors became a major project (Felt/Wynne 2007). One guiding principle articulated in this context is the one of Responsible Research and Innovation (e.g. von Schomberg 2013). While preparing for the next Framework Program "Horizon 2020", the idea of participation gets ready to be mainstreamed in the whole area of EU funded research and innovation. The main idea behind is to create acceptance for innovation while innovating and by making transparent the functional and cultural foundations of a new technology and to constitute "co-responsibility" (von Schomberg 2013:#) of civil society actors. This idea is not really new, but the long-ranging scope and the

specific role-definition for civil society actors is unprecedented. These strategies in the political arena are curtailed regarding the complex governance architecture of such processes. Therefore, it is important to analyze these specific strategies against the background of the complex governance structure. The concept of governance allows both to analyze empirically processes of social coordination (like the inclusion of civil society actors in research) and to evaluate these processes regarding specific norms of democracy (like transparency, participation or legitimacy).

Against this background, the purpose of this paper is to analyze contemporary modes of Civil Society Organization's (CSO) participation in the scientific research by combining an empirical study about the inclusion of CSOs on the basis of a survey of all European Commission FP7 projects with a theoretically elaborated analytical grid to bring out the aspects determining the current landscape of participation practice. Our research problem is based on the hypothesis that there are a variety of practices of CSO participation in research leading to governance problems. The participation of CSOs in research is embedded in a set of assumptions and procedures which affect the achievement of internal or external expectations. The analysis of expectations and their fulfillment drives us to consider the relationship between research governance (as a way of reaching or managing expectations) and the means of expressing interest (public interest).

The argumentation of this paper is based on four steps. In a first step, we discuss some main perspectives and results from the governance literature in general and the one focused on science in specific. While doing so, we want to underpin the necessity of a research doing both looking empirically at practices of governing of and through participation of CSOs in research projects and constructing an analytical grid to evaluate such practices with respect to specific ideas of governance (like 'reflexive governance'). In a second step, the structure of such an analytical grid will be elaborated. Main aspects are firstly the structuring of a normative perspective and secondly the development of a normative-empirical scheme for research. In a third step, the specific methodological approach will be discussed and some intermediary results will be presented. They show that the scheme to analyze participatory governance processes within research is working well. Moreover, they indicate some specific governance problems. Therefore and within a fourth step, we will not only analyze the results of the survey, but also outline specific governance problems which should be made a research problem within case study research.

Governance of research as participatory governance of research?

After quite a long period of regulation skepticism, the idea of Governance marks an important reorientation of this debate (overview: Grande 2012). This career is surprising looking at the 'two-sided character' of the concept; on the one hand it's a normative concept aiming at 'good' governance, on the other hand it's an analytical concept describing the restructuring of forms of reigning. In analytical terms the concept focuses on the diverse and often multifaceted architectures of regulation between different levels of regulation (like community-level, Nation-state, transnational regimes), different actor fields important for regulation (such as politics, economy, civil society) and mediums of regulation (e.g. law, money, discourse). The idea is to reconstruct 'structures of complementarity' between the different levels or actor fields of regulation. Actors are intertwined by complementary relations of regulation. Against this background, the concept of Governance focuses mainly two aspects. Firstly, the perspective of Governance of research set out the position that fields of regulation are not structured by the dominance of one actor of regulation, but is determined by the polycentric arrangement of heterogeneous actors of regulation. The options to design the field of regulation in one's own sense are increasing in the same extent in which the respective (collective) actors dispose of internal capacities to bring the own perspective into the discourse and to cope with the perspectives and demands for regulation of the other actors present in the field. Secondly, the Governance perspective looks at the forms and meanings of complex 'architectures of regulation'. Within this perspective, there is a sensibility for the differences between distinct forms of regulative interventions. Guidance is not only realized by localizing resources (especially money), but also by framing of social activity within discourses.

With regard to the field of science, the governance perspective especially focuses especially on new forms of regulation in the production of scientific knowledge (e.g. Lyall/Tait 2005; Jansen 2009) and the structure of the production system of scientific knowledge (e.g. universities or the cooperation between different research organizations). Moreover, the debate about "democratization of expertise" (e.g. Maasen/Weingart 2005) or about participatory governance of research (e.g. [START_REF] Wehling | Uneingeladene" Partizipation der Zivilgesellschaft -ein kreatives Element der Governance von Wissenschaft[END_REF] shows that some guiding classifications and divisions of labor are undergoing a fundamental change. One of these classifications is the division between experts and laypeople. In the meantime, civil society actors are regarded as important for conducting problem-oriented research (e.g. Frickel et al. 2010). What are the arguments? Concerning functional considerations, civil society actors are seen to broaden the scope of analysis, because they form topics, they provide specific knowledges or objects or they allow mediating between knowledge and values. Concerning social considerations, civil society actors are seen to develop higher acceptance in innovation processes. This is why the inclusion allows mainstreaming of specific views on topics in the public discourse. Thus, these views get normalized and regulated with regard to their desirability. Concerning political considerations, civil society actors are seen to improve legitimacy of innovations. Their general orientation toward the public welfare is important here, but also the improvement of transparency through participation and finally participation as a form of building up legitimacy.

One important argument to be made here is that chances of improving social coordination in fields of problem-centered research are depending on the fact whether the boundaries between the fields (science on the one hand, civil society on the other) and their institutional identityproblems are fairly addressed. Against this background, the question for governance is not only a question of better coordination and regulation, but also a question of institutional identitypolitics. This can be exemplified with regard to science. To include civil society actors into science is not only bound up with hopeful promises, but also seen as a source for far ranging fundamental problems. This is founded on the fact that science is institutionally based by the approach not being oriented towards specific interests (locus classicus: [START_REF] Merton | The Normative Structure of Science[END_REF]Merton /1973)). By including interest groups, irrespective of their specific orientation (whether economic or public interests), a problem of boundary-work emerges. This boundary-work has to secure the institutional foundation of science while open up science for the 'productive' parts of the cooperation with civil society actors. Such 'productive' parts can be seen in the addressed aspects (functional, social and political) before.

Against this background, there are two important aspects of the problem of identity-politics to be discussed. First, one has to take into consideration different forms and logics of civil society actors participating in research. This depends mainly on the question: who are the actors participating? This seems to be obvious, but it is not on the viewpoint of a systematic perspective. It is of major importance, whether the actors included are single-person-movements or organizations, or whether they are dedicated to specific goals or an overarching interest. Another aspect is the quest for different forms of participation in research: How can these different forms be classified? This is why, forms of participation are mirroring the social dynamic behind the concrete process and uncover the logics and needs of identity-political strategies. Second, it is important to look at the content. What are the themes and ideas, the norms and values put forward in the collaboration process and why they are important with regard to the respect groups (scientists and civil society actors). The main point here is that within the collaboration process both facts and values are hopelessly intertwined. Therefore, analytically important, a normative-empirical framework for research about participation in research is needed. In this chapter we only focus on the first aspect, the second one will be addressed in the following chapter (chap. 2).

Who are the actors of participation? Who is seen as a CSO, who not -and why? How can the different actors of participation be classified? To Euclid Network, a CSO included in our project team, CSOs are "defined as organizations that are non-governmental, not-for-profit, not representing commercial interests, and that pursue a common purpose for the public interest. In the EU, the concept of civil society encompasses a wide range of organizations: including all non-market and non-state organizations and structures in which people organize to pursue shared objectives and ideals. Not just NGOs, but encompassing cooperatives, associations, grass-roots, mutuals, not-for-profits, foundations, think tanks and umbrella organizations". Looking at this definition and argumentation, one can get an insight about the difficulties describing the entity 'participant'. One important aspect is the degree of being organized and the form of organization. We focus here on CSOs regarding two arguments: most of the participation activities are done by organizations, single-person participation is a really marginal phenomenon. Moreover, with aiming an analysis of governance structures and processes, the focus on organization and degrees of being organized is more useful. A second important aspect is connected to the question, what each CSO is representing in the concrete case. Do they represent specific interests or are they oriented towards public interests. In many cases, CSO were by definition seen as representatives for non-economic interests. But is this terminological fixing useful with regard to the reality of participation? We think that it is important not to start with such a fixing, but with an idea of the different forms of representing public relevant interests in the broadest sense. This can be natural conservation -but also economical welfare. Against this background and regarding the participation of CSOs in research, the concept of CSOs should also encompass organizations that represent -in a second order -commercial interests. It seems to be important to engage into a conceptual framework which does not narrow the view on a specific excerpt of the social reality of participation (Kohler-Koch/Quittkat 2011).

Are there different forms of participation in research and how to classify them? In this section of our argumentation we would like to put forward two idealtypical distinctions. Firstly, the one between invited and non-invited forms of participation [START_REF] Wynne | Public Participation in Science and Technology: Performing and Obscuring a Political-Conceptual Category Mistake[END_REF][START_REF] Wehling | Uneingeladene" Partizipation der Zivilgesellschaft -ein kreatives Element der Governance von Wissenschaft[END_REF]. This distinction addresses one important aspect of the structural dynamic. Invited forms of participation are aiming to include CSO with regard to a pre-defined research agenda and under the auspices of goals defined by scientists. These ways of participation can serve all the three forms (functional, social, political) of improving the outcomes of research. But it is expectable that scientists are the leading actors in this process. One can imagine that this form of participation is to be found in many cases, because it allows to improve the situation of research without calling into question the division of labor and governance structure (cp. [START_REF] Brown | Emobdied health movements: new approaches to social movements in heath[END_REF]). Un-invited forms of participation are more complicated with regard to the governance structure. In such cases, the CSOs themselves formulate a starting agenda for research focusing on specific collectively relevant problems. E.g., patient organizations were relatively successful in setting research agendas in the past (Frickel et al. 2010;[START_REF] Brown | Emobdied health movements: new approaches to social movements in heath[END_REF]. In this way, new topics could be formulated for science, but the question of who is setting the rules becomes contentious. While making research in cooperation with CSOs or for a specific community, the process of self-defining the scientific agenda by scientists can be interrupted. Against this background the emergence of a multifaceted conflict-dynamic is more likely. And therefore it is to be expectable that the governance structure is changing.

Secondly, a difference is to be made between typical arenas of knowledge-production [START_REF] Brown | Environmental Causation of Breast Cancer and Challenges to the Dominant Epidemological Paradigm[END_REF]. They argue against the background of empirical evidence in the field of health care movements that three forms of arenas are to be distinguished. The first typical arena is "Doing Scientific Research". In this arena the focus lays on "how scientists choose particular topics and questions, how they proceed with their investigations, and how they interact with funding, research, and support organizations" (ibid.: 505). The second typical arena is called "Interpreting Science". In this arena the question is addressed, which conclusion can be drawn by scientists from their empirical findings. Empirical findings are not simply facts, to the contrary they are part of interpretation processes more or less conflict-ridden. Therefore, in this arena questions of the "standard of proof" are under debate. What is the "weight of evidence" regarding the different findings? What are the criteria for validity and significance to evaluate the different studies (ibid.: 507f.). The third typical arena is called "Acting on science". In this arena the main point is the question for the political consequences of research. In many cases this arena is highly intertwined with the first one. The agenda setting depends heavily on the political course behind, e.g. whether the precautionary principle is used and if yes in which way.

Both distinctions can be seen as a heuristic tool for analyzing forms, structure, logic of participation processes of CSOs in research. They allow to describe and to classify what's going on in research while participation of extra-academic groups are included and more or less allowed to steer the direction of the endeavor. These heuristic tools allow a better description of participation in research, but they give no explanation why the form and structure of the governance process is as it is. The next important point is therefore to combine these considerations with ones giving an analytical structure.

Analytical Grid of participatory research governance

One important point regarding the differentiation between invited and non-invited forms of participation was not yet addressed. Behind most of the ideas of participation there is a classification pre-structuring the form of participation. This is highly influential with regard to setting the stage for ideas of deliberation. This can be described as follows: "Participants are conceptualized as citizens or laypeople, meaning that they are interpellated as individuals, not as members of an organization or an interest group. In addition, their main qualification is exactly their ignorance concerning the issue at stake and, at the same time, their amenability to education." (Braun/Schultz 2010: 409) This classification of "the participant" was used in a lot of participatory Technology Assessment (pTA) procedures. In the meantime the limits of such an approach are obvious. Moreover, with regard to aspects of governance and democracy such a view on deliberation is disputable. (cp. Hess 2011). This is why, the specific qualities, competences, cultural capital and specific interests are not seen as a substantial part of participation, no matter who is the participant (individual or organization). Moreover, these specific capacities are seen as a barrier for an effective and legitimate participation. But to the contrary, these capacities have to be seen as a quality with regard to effectiveness and legitimacy of research. While articulating particular interests, CSOs offer a view on generalizable public interests.

Obviously, there are specific presuppositions regarding the character of participatory governance. These presuppositions are embedded in the fundamentals of governance and democracy theory [START_REF] Rainey | Analytical Grid. CONSIDER-Project Deliverable 1[END_REF]. "Against this background, there is a systematic problem inherent analyzing processes of participation with regard to the addressed goals of enhancing effectiveness, legitimacy or accountability. Therefore, the construction of a specific tool for analyzing participation processes in research as participatory governance is needed. Thus, we argument for a specific form of an analytical grid. This is important with respect to the fact that the normative argumentation to underpin participatory measures is in many cases either misleading to the grounds on specific presuppositions or too abstract for being directly used for the evaluation of the measures under consideration (ibid.). Deliberation, transparency, legitimacy -these are important values, but they have to be specified for the normative and empirical analysis of participation processes. Such a specification is needed to grasp the character and quality of participatory governance and to give hints for improving such processes.

Our thesis is that we have not only to direct the research question to a normative-empirical approach, but also to build up an analytical framework allowing to address both the normative side of the question and the empirical one. The specific difficulty of such a normative-empirical research about the participation of civil society actors in research needs to be addressed by a specific presentation of the problem. To get insight into the governance effects of participation, the focus of research has to be oriented towards the processes of norm construction while participation is going on. What are the norms important while participatory conducting research, how are they designed and brought into action and what are the aligning orders of classification and justification? Against this background, we have given ourselves the following question: How do actors define and reach their expectations related to defining public interest when constructing norms in research projects?" (Goujon, Reiney, 2012) As it is the case with the sketched aspects of participation in research projects above in chapter 1, answering the question should enable us to explore how two assumingly divided societal spheres -science and civil society -could be incorporated in the frame of a research project. We expect that the concrete forms of practicing participation mirror not only the epistemic status of the respective research project but also the logics of the different social spheres. This process can be reconstructed in terms of Bourdieu's field theory. Not to go here into a more detailed analysis of this specific theoretical background, in the following part of this section we would like to put forward some specific aspects which are mainly dedicated to the empirical analysis in the following chapter (chap. 3). These are: (a) the specific entrance point for this normative-empirical analysis is the analysis of expectations; (b) the normative differentiation of participation goals and (c) to develop a scheme for the empirical analysis of participatory governance in research projects.

(a) specific normative-empirical entrance point: expectations. The sociology of expectations has made clear that while analyzing expectations core classifications of ordering the social spheres are implicitly expressed. These classifications are related to the values to be secured, the form of cooperation to be acceptable, the knowledge which is important and valuable and the social norms politically to be processed. In this sense, processes of cooperation between researchers and CSOs can be seen as a social laboratory to construct and re-construct specific forms of knowledge production while solving social pressing problems. By doing so, not only cognitive problems are solved but also social orders are constituted. Expectations are working like a burning glass to focus on both aspects in the same analysis. In our analysis, there are different idealtype forms of governance, from a "standard model" of governance to a "deliberative inclusive" one (Rainey/Goujon 2013:8f.). These are quite different with regard to the overall expectations expressed in it. While the first one is oriented towards 'managing the relations with the public', the second one is based on 'processing accountability, legitimacy, fairness'.

(b) goal of participation: which forms are dominant? While including CSOs in the research process different goals can be take into consideration. As research projects are divided in different phases, e.g. knowledge production or diffusion of knowledge, the respective role of CSOs should differ considerably. Depending on the stage of project the inclusion of CSOs can offer distinct options. At the beginning aspects of agenda setting or inclusion of knowledges are important and with this questions of effectiveness. At the end of a project, aspects of mainstreaming the findings become more important and therefore questions of fairness and legitimacy. In any case, the relatively abstract norms are to be analyzed empirically in their practical effects while governing research projects.

(c) scheme for the empirical analysis of participatory governance in research projects. With regard to the two points before, we think that a specific scheme for structuring the empirical work is needed. On the one hand side, such a scheme should allow us to select different cases of interest for an in-depth analysis. On the other hand side, this scheme should allow us to explain why specific forms of participation are dominant or why specific problems in participation occur. To explore and analyze the cooperation in research projects, two dimensions seemed central according to our normative-empirical entrance about expectations. This allows us to analyze and to create a typology that will highlight the different patterns from our fieldwork while relating it to main governance models coming from literature. Our first empirical data (quantitative survey of all FP7 projects) indicated different tendencies that we have synthesized in new intermediary hypothesis. The leadership dimension is central to understand the management style of the consortium observed. The different styles of leadership of research projects will be linked to different practices of CSO participation. The different leadership of research projects might also be linked to different presuppositions/assumptions, procedures which affect the formation and achievement of expectations. The other dimension, intensity of collaboration explores how the interacting actors are constructing norms in context i.e. how they communicate and discuss their roles according to their cultures and the nature of their knowledge. Different intensities of collaboration are linked to different governance problems and structures. Intensity of collaboration is also linked to the normative problems related to conflicts arising from different epistemic cultures.

The empirical representation of those two dimensions of CSOs participation is then possible.

Screening: Survey of all FP-7 Projects

To understand the dynamics of CSOs participation in research we did investigate the characteristics of the FP7 research projects. How CSOs are currently practicing research? Our data collection includes two different surveys, the first one was sent to 14 000 FP7 project coordinators and we received 2959 completed responses. Our response rate is 21%, which is a good one. There are two important aspects to be discussed: (a) methodological considerations and the construction of the survey; (b) some elected findings from the survey analysis.

Methodological Considerations -construction of the survey

Building on the theoretical frame already presented below in this paper, that were synthesized in an analytical grid, the quantitative data collection was designed to allow the quantitative analysis to be used for exploration of the field -and identification of the main patterns of CSOs participation. Why using quantitative data in an explorative way rather than for validation purposes only?

First the huge numbers of projects, topics and types of CSOs included in FP7 research projects required to get an overview about the total number of CSO-participation in FP-7 projects, and its main characteristics; this description of the research field validates our initial theoretical framing and allows us to refine it. Thanks to that survey moreover, we get some important insights with respect to general aspects and forms of participation and the structure of cooperation. Using parameters selected thanks to the theoretical landscape and problematization of our research question we were able to fine tune questions to better understand the roles, motivations and expectations of the actors involved. We also get the well-grounded selection of parameters of selection criteria for further investigation (case studies).

We surveyed all (15,000) FP7 projects across all areas of research in order to determine the current landscape of CSOs participation practices. It was implemented via an online survey. Surveying all FP7 projects instead of focusing on certain areas, this research strategy allows us -in a later step of the project -to develop comprehensive guidelines for the relevant stakeholders, only reachable through a comprehensive coverage of all projects.

We sent two different questionnaires. The first one was very short (max. 5 questions) and was sent to any of the 14 000 FP7-project coordinators. Then 414 out of the 455 FP7 coordinators that acknowledged CSOs participation in their research project and agreed to further participate to the survey were sent a second questionnaire, and we received 162 responses completed. Finally we asked those 162 respondents if they would give us a contact of one CSO partner of their project. We then sent our second questionnaire to 78 FP7 projects CSOs contact and we have already received 20 responses. Sending the questionnaire to two different people per project (project coordinator and CSO member) allows us to focus on the normative construction of people's expectations. It enables us to cross their social representations of CSO roles and thus to address the implicit normative framing issues.

Only a few project coordinators indicated to us the contact of the CSOs participating in their projects (78 out of 162 respondents). This is still nearly 50% of this group. We found out that one of the issue was the elusiveness of the notion of Civil Society. Apart from the fact that we already demonstrate in chapter 1 that CSO denomination include a wide range of actors, Civil Society is inherently difficult to conceptualize and operationalize. According to Heidbrader (2012:p 4) the general definition of civil society "oscillates between a number of basic concepts. One school defines Civil society as an actor whose democratic role is understood as a counterpart and in opposition to formal governmental power". The second conception sees civil society acts like a collaborator, an integrated player in political processes. Third, civil society might be defined as constituted by a communitarian conception of civility. Linking these notions to the one of participation, "the notion of civic participation is rooted in the normative assumption that the efficiency of any economical, management and other social system as well as the legitimacy of democratic political systems depend on the involvement and participation of the public" (Matonyte, 2004). The elusiveness of the civil society concept, along with the huge amount of literature available, for instance see Van Roy (1996), led us to give a general definition of Civil Society Organizations (CSOs) in the introduction to our first questionnaire. This definition is: "By CSO we mean non-governmental, not-for-profit organizations that do not represent commercial interests and pursue a purpose in the public interest (for example NGOs, cooperatives, associations, grass-roots, mutuals, foundations, think tanks and umbrella organizations)". How this definition is used? This definition is not so easy to grasp as we received some questions from our respondents who did not know whether their organization could be defined as a CSO. For instance, some universities claimed they were CSOs which does not fit in our definition (for us they are scientific and public entities linked to the government)1 . We also received questions about Entities identity from private research institutes. Some of them thought they were part of Civil Society and some others did not. More intriguing, some Project coordinators answered our second questionnaire, because they claimed their project did include CSOs and when we got in touch with them for in depth investigation they answered that there was no CSOs involved, but scientific institutes. This is very important both for rethinking the concept of CSOs and for constructing policy guidelines definitions. What is the specific address of European Commission policies then? A lots of research governance rules and regulatory texts such as Framework programs, strategies, or Horizon 2020, are taking for granted the fact that CSOs are an homogenous and static group of actors. Our results show that there is a difference among cultural settings, linked to the public/private sectors delimitation that varies a lot. Typically in Latin countries where democratic regimes are well established and combined with a centralized vision of the state -e.g. Greece, France, Italy, Spain -CSOs are seen mostly as a counterpower. By contrast with Nordic countries, where a federalist vision of the state is more dominant and CSOs are mostly seen in a communitarian tradition.

Are people aware of that? Identity politics are embedded in this difficulty to draw boundaries between private and public spheres and between actors: who's in ? Who is in the "other part" is unclear for the scientists.

Selected results from the study

We present some of the most interesting and striking findings of the empirical research from Survey one and two. Interesting means: what are general patterns of participation to be seen in these findings, what are the differences in the expectations between the two groups (researcher -CSO)? What are governance problems resulting from these conflicts?

(a) CSO involvement and role definition

The first two questions were focused on: are there any CSOs included in research? And: What roles were dedicated to CSOs participating in research projects? As Figure 1 shows, 21% of the total number of the respondents answered they collaborate with at least one CSO. The roles of CSOs in the project are diverse, according to the respondent of our initial survey (multiple choices answer), like figure 1 shows. The main one is to provide expertise, be a member of the team, results discussion or contributing to publications. Compared to the CSOs involving project coordinator responses (questionnaire 2), CSO roles here are more focused on information activities (local knowledge, facilitating information, contribution to publications) than in the more participative research projects. The second questionnaire gives more insights on the role of CSOs. The multiple-choice question shows clearly that CSO roles are perceived as being fundamental when they give their expertise and when they disseminate the project results and guidelines. Expertise here isn't coming from lay people as we underlined in our sample description that CSO members who answered our questionnaire are well educated and skilled in research projects. CSO members' value added seems to help the research project get more contextrelevant for policy or other beneficiaries (patient, children etc.) needs.

The traditional model of roles distribution between researchers and stakeholders usually implies that CSOs should disseminate the results. The latter are perceived as go-between which are going to translate and to pass on the produced knowledge or to test the developments of R & D. Nevertheless, if in our case 75 % of the project coordinators, did assign the dissemination role to CSOs, they entrusted this responsibility to another consortium member. This can correspond to a professionalization of this activity which could be more and more confided to a partner specialist of the project management. It could also be a characteristic of the FP7 projects, the success of which is more and more depending on organizational and project management process (quality insurance plan for instance). Most interestingly, the mutual representation of CSO roles differs when considering CSO involvement in the project. According to CSO members' responses CSOs are initiators of the project more often than what PC acknowledge (50% / 19% responses), as well as CSO members claim to be advisory board members more usually than PC mention they are (50% / 29%). This tends to indicate a tendency for project coordinators to assign a more passive role in the project to CSOs members, which does not seem to suit CSO members pointing their initiatives. These different perceptions of CSO involvement in research activities may indicate a normative framing conflict about what ought to be CSO role inside the research team. This conflict is not about their skills, if we refer to the fact that the first role attributed to CSO members is their expertise. Besides they seem to be also seen as researchers (39% of the PC agreed on that stance / 33% of the CSO). The tasks reserved to other members of the team are setting the research method and policy development, according to both respondent categories. This is more a governance conception discussion: should the project coordinator take the leadership, or should the project governance be more participative? The CSO role attribution also indicates that CSOs are scarcely able to discuss the research project design from its start. Only 30% of project coordinators indicate that CSOs are involved from the start of the project. The majority report that they are involved at the planning stage only which is confirmed by CSOs member responses to the questionnaire (second survey question 5).

The decision not to resort to CSOs in one consortiums of research seems to be connected to a positivist vision of the scientific validity, it seems also bound to the funding scheme and to the fact that it is doubtless even simpler for certain research teams to escape the integration of CSOs, because the planning of the project and the modes of collaborative work can turn out complicated as we are going to show in the following part

(b) CSOs and researchers modes of collaboration

Second question, how is the team working and meetings are organized, how do people communicate with each other? First of all, the ways people inside the teams meet and communicate with each other are relevant to understand better the way they work together. Main FP7 consortiums include teams from different European countries, which means long distance work. The consortium might also have to coordinate the work of many partners (up to 50 in some cases). Do team members meet regularly face to face or do they work independently and meet once a year? Work packages structure may be designed in a taylorist way, implying people working independently of each other, without any common tasks, which would not allow collaborative work. Another characteristic of our sample is that only a few (26%) had experienced a prior project directly linked to the actual project (second survey question 9), which means less experience of working together. But project coordinators already had the experience of working with CSOs in a research project (58%) see table below and 96% CSOs members had experienced a research project before. There is a high level of pre-existing cross socialization.

It appears that according to the coordinators of project including CSOs (second questionnaire), 51% of the teams physically meet twice a year. This is seldom occasions, which one can think might focus on research developments rather than collaborative work. 31% meet once a quarter, a frequency that makes team-work more likely. It appears that there are not so many teams able to actually work in a collaborative way. Is this a clue on their governance model? CSO involvement in research project need a minimum time spent to discuss and share research concern, in order to create a common language and a common vision of the project aims. Meeting frequency might be an indicator of the research governance model at stake in one project. To deliberate, people need to meet and discuss options before the decision process itself takes place.

In this section we demonstrate that FP7 projects have certain characteristics that frame the working and communication context of each research team. There is a link between most usual communication means and physical meetings frequency on one part, and working organization on another part, especially concerning leadership and decision process: dialogue is a central notion in governance and suppose regular meetings. Its deployment in terms of inclusion is important to grasp as well as its consequences on decision-making process. Here 31% of our 149 CSO including teams seem to be able to develop a collaborative working organization and thus might be able to act in a participative governance model. It gives us an idea of what proportion of projects could refer to participation processes, as the minimum conditions are present (pertinent work organization) even if we would need to go further to deepen our knowledge about those research projects.

(c) Main expectations about project achievements

The third question was addressed to what are project coordinators and CSO members main expectations about project achievements. As we already demonstrated, there is a thin boundary between project coordinators and CSOs members backgrounds (holding phds, skilled and experienced in research projects). As we wonder about each partner expectations and how those expectations are reached through research outcomes bestowing general public good, we will keep in mind that categorization may lead to naturalize existing socially built categories and that they might even influence the way we grasp reality.

A way of getting insights about people's expectations is to ask them what they define as initial outcomes of the project, as opposed to the actual achievements reached at the end of the project. According to both project coordinators and CSOs members the first initial outcome of the project is to enhance scientific knowledge (75% and 50%). They both are also keen on policy outcomes. This is congruent with the expectation of project coordinators that CSOs will provide information that will enhance their project (see CSOs role first section (figure 3). Then, what are the mostly expected final outcomes? For CSOs members the final outcomes should mostly enable them to give advice to decision makers (75%), then enhance scientific knowledge and help people not participating to solve a problem. The main beneficiaries are often team members themselves and industry (58%) and then European commission and their own organization members. Project coordinators expect to enhance scientific knowledge (67%) and to be able to give advice to decision makers (61%). Those differences in terms of expectations are important, and show that CSOs members expect to enhance scientific knowledge and in doing so might expect to affect the trajectory of a research project. The research background might also give sense to those data, in a drive to include more CSOs within the projects. CSOs members are also pointing at industry and European Commission as central beneficiaries of their research project outcomes. Their expectations are more often to give a contribution to societal needs than PC's. They both (PC and CSOs) consider of great importance to be able to contribute or to influence decision-making processes. Those who expect to help external stakeholders (80%) or to produce new scientific knowledge are often more optimistic about their objectives achievement, as they say they will achieve or have achieved their project objectives. CSOs members are less confident in the project capacity of reaching its objectives, they are only 25% (against 72% of PC) thinking the objectives of the project have been or are likely to be achieved. This underlines the fact that as leaders, project coordinators might be very confident, but also highlights the different ways of assessing the project results, linked to actors expectations and aims; and at that point values seem to differ. It seems more difficult for CSOs members to reach their expectations. There are different interpretations here of what should be the kind of knowledge produced. There are also alternative and a contradictory perception of the research project's main objectives and the way to fulfill them.

In this section we demonstrate that expectations are diverse. CSOs members expect to enhance scientific knowledge and in doing so might expect to affect the trajectory of a research project. This remains difficult as they do not feel their expectations are reached. The research background might also give sense to those data, in a drive to include more CSOs within the projects.

Discussion research participation and governance problems

The standard model of science is dominant in the responses we got in survey 1. It is "a traditional top-down approach, which is based on the knowledge of experts. Normativity here comes from the knowledge and opinions of the experts involved in the decision-making". [START_REF] Rainey | The Theoretical landscape[END_REF] CSOs involvement in research is still embedded in a rather classical normative setting of research as to their role and attribution. FP7 projects have certain characteristics (length, international collaboration, funding scheme, evaluation, etc) that frame the working and communication context of each research team.

CSO roles are perceived as being fundamental when they give their expertise and when they disseminate the project results and guidelines. Expertise here isn't coming from lay people; as we underlined in our sample description that CSO members who answered our questionnaire are well educated and skilled in research projects. CSO members' value added seems to help the research project get more context relevant, for policy needs, or other beneficiaries (patient, children etc.) needs. There is a clash among academic institutions and CSOs members according to CSOs roles.

The CSO role attribution also indicates that CSOs are scarcely able to discuss the research project design from its start. Only 30% of project coordinators indicate that CSOs are involved from the start of the project. The majority report they are involved at the planning stage only which is confirmed by CSOs member responses to the questionnaire (second survey question 5). They seem to "slot" in a predetermined format, or be more in a position to discuss a pre-defined plan. Considering that CSOs claim they are sometimes initiator of the project (see above section); it might be that they are involved during all the lifetime of the project but that they lose leadership on research agenda setting, and research method.

The project governance here is closed to a functional one: tasks division and specialization among partners which is supported by an implicit definition of science. Here the interaction between partners is more aggregative than deliberative. Project coordinators seem to see CSOs more as end users representatives than as equal partner. CSOs scarcely define the research method and agenda and are perceived as experts. There might be a norm construction process here about what CSOs role and researchers role ought to be, and implicit power relations. CSOs are valued for their expertise and their network, which will facilitate the dissemination of the results as well as the test of the developments. Nevertheless they are also invited to the academic conferences and to the meetings of project. Researchers usually master the project research methodology and agenda setting of the research problem.

FP7 projects have certain characteristics that frame the working and communication context of each research team, and that reveals some embedded assumptions. It seems that CSOs are not thought as central actors because there are a few incentive schemes designed for CSOs participation. The research background in terms of research governance tend to be oriented towards the Social research model: Civil society is consulted about its views on a public policy or research goal owing to its function as a non-state actor 'representing' public concern or interest in that particular issue.

Among CSOs involving projects a few calls made CSO participation compulsory (6,45%) and only 16,95% proposed specific incentives of CSOs in the funding scheme. As there are only 30% of the projects benefiting from multi-funding, the Seventh Frame Program does not seem to be very appealing for CSOs involvement in research project.

There seems to appear another pattern of CSOs participation in research project as the main expectations are shared among researchers and CSOs members. According to both project coordinators and CSOs members the first initial outcome of the project is to enhance scientific knowledge (75% and 50%). They both are also keen on policy outcomes.CSOs members expect to enhance scientific knowledge and in doing so might expect to affect the trajectory of a research project. 31% of our 149 CSO including teams seem to be able to develop a collaborative working organization and thus might be able to act in a participative governance model.

When the funding scheme of the project included incentives designed for CSOs (either making their participation compulsory either financial incentives for them), CSOs contribute greatly to the project: they are more likely to contribute to the research agenda setting, they are seen as equal researchers or they initiate the project.

Figure 1

 1 Figure 1 Survey 1 : "was there any CSO participation in your research project ?" (Source: Survey 1 CERAPS, Lille 2 University)

Figure 2 :

 2 Figure 2: role definition of participation (Source: Consider Survey 2 CERAPS Lille 2 university.

Figure 3 :

 3 Figure 3: Second survey question 25 project coordinators' responses in % ("what are the initial outcomes of the project")? Source: CERAPS, Lille 2 University

Figure 4 :

 4 Figure 4: second survey question 26 project coordinators' responses in % ("what do you think the final outcomes will contribute to?" multiple choice) Source: CERAPS Lille 2 University.

We have decided to check every response we received to identify if the CSOs were actually CSOs according to our definitions. We found out that some 10% did not fit in our definition.