Yves Buchet De Neuilly

EUROPEAN EXTERNAL RELATIONS FIELDS: THE MULTI-PILLAR ISSUE OF ECONOMIC SANCTIONS AGAINST SERBIA

Observing the uses of economic sanctions is one way to analyze the links between external economic relations and foreign policy 1 . Actors and commentators have for some time deplored the gap between the European economic strength and its political weakness. The series of sanctions imposed against Serbia from 1998 to 1999 are one attempt of the EU to fill this gap and to transform its economic size and weight into a real and effective foreign policy. It was not the first example of EU sanctions (see De Wilde d 'Estmael 1998, Nuttall 1992), but it was the first time that EU sanctions were taken autonomously. In other words, these sanctions were unique because they were not simply adopted out of UN Security Council resolutions.

European governments were heavily criticized for their impotence and divisions during the Yugoslav wars. As a result they were prompt to respond rapidly to the troubles in the Serbian province of Kosovo. In addition to diplomatic contacts and mediations, they decided to strengthen their action by using sanctions as a tool. However, the aims of the EU in the beginning of the Kosovo crisis were not as clear as expected. There was minimal consensus that sanctions should in principle be taken, but there were strong dissensions about their effectiveness and scope. The European strategy was not always clear. For example, while EU Foreign Ministers announced tough sanctions, they later signed regulations that did not have any economic impact.

The aim of this chapter is to provide a satisfactory explanation of this apparent contradiction in the EU sanction policy. It argues that the realist analysis is little help, despite the fact that EU common and foreign security policy (CFSP) is generally considered to be an intergovernmental area . In this case, divergences came not from different states but from representatives of the same states. The defence of diverging interests was not the result of individual or bureaucratic disagreements, but rather a consequence of the existence of several autonomous external European bureaucratic spaces or sub-fields 2 where interests of member states are expressed 3 .

The principle judgement to sanction was always decided in the institutional framework of the CFSP pillar of the EU Treaty. Contrary to common interpretations it was easy to reach a fast agreement under this pillar. On the other hand the decision to implement sanctions under the first EC pillar, was characterized by endless meetings in the Council and a series of tight discussions. The shift of sanctions decisions from pillar two to pillar one led to deep changes in the conditions of negotiation, not only in terms of constitutional structure but also, and more decisively, in terms of actor configuration and structural distribution of power both at the member state and at the European level. This chapter has a number of aims. First, I will briefly summarize the EU diplomatic reaction to the escalating situation in Kosovo (in the spring of 1998) and the use of a sanction strategy generally. I will then highlight the apparent contradictions between the tough political declarations of the European governments and the empty shell of the economic sanctions. Third, to understand this discrepancy, I will show the necessity to identify the relevant space structuration of the EU external relations field. This will lead me, finally, to explain the decision to impose EU economic sanctions as the result of negotiations in relatively autonomous European sub-fields.

SANCTIONS AS A EUROPEAN RESPONSE TO THE KOSOVO CRISIS

Troubles in the Serbian province of Kosovo intensified in early March 1998. A Kosovar Albanian demonstration was barely repressed by the Serbian police, and at least 80 civilians were killed. For the first time the events in Kosovo raised a unanimous indignation in the international community. The Foreign Ministers of six major diplomatic countries met on 9 March in the framework of the ad hoc Contact Group for the former Yugoslavia. The Contact Group had been set up in 1994 to co-ordinate the positions of the United States, the Federation of Russia, the United Kingdom, France, Germany and the incumbent president of the European Union during the war in Bosnia-Herzgovina (Boidevaix 1997; 64). After the Dayton Agreement, the Contact Group's activities resumed, focussing on the tensions in Kosovo.

On 9 March 1998, the Contact Group condemned the large-scale police action of Serbia and decided to take new measures to increase pressure on the authorities in Belgrade. Ministers called for: a) UN Security consideration of a comprehensive arms embargo against the Federal Republic of Yugoslavia (FRY); b) Refusal to supply equipment to the FRY which might be used for internal repression or for terrorism; c) Denial of visas for senior FRY and Serbian representatives responsible for repressive action by FRY security forces in Kosovo; d) A moratorium on government financed export credit support for trade and investment, including government financing for privatizations in Serbia. [START_REF]Statement on Kosovo[END_REF] The Foreign Ministers used a stick-and-carrot strategy. They gave the FRY President Slobodan Milosevic ten days to withdraw its special police units and to cease security force action. They threatened to impose further international sanctions and, specifically, to pursue a freeze on the funds held abroad by the FRY and Serbian government. This tough statement was not easily finalized and had to be translated into binding norms before it could have a concrete effect. This was the case in part because the Contact Group had no legal statute and depended on other international or national arenas.

The attention immediately turned to the UN Security Council where substantive discussions on sanctions began. Drezner (2000) argues that countries often look for broad multilateral support in such diplomatic processes. In New York, however, the divisions expressed during the preparation of the Contact Group statement were more acute. The Russian Federation's hesitation slowed the decision-making process, making the probability of adopting measures within the Contact Group deadline highly unlikely.

In the end, the European governments decided not to wait for the UN Security Council Resolution and instead took autonomous sanctions for the first time (Karagiannis 1999). A draft common position was drawn up by the CFSP Counsellors' working group of the Council and finalized by the Comity of Permanent Representatives (COREPER) in Brussels. Thus, the Contact Group's deadline was met when the Council adopted a common position on restrictive measures against the FRY on the 19 th of March. 5 In line with the Contact Group's recommendations, sanctions consisted of an interdiction to supply equipment for internal repression, a moratorium on government financed export credit support for trade and investment and a visa ban for senior FRY and Serbian representatives.

The next week, on 25 March, the Contact Group noticed some movement in Belgrade's position. [START_REF]Statement on Kosovo[END_REF] Talks were beginning on the autonomy of Kosovo, and an agreement seemed to be reached on the implementation of an education accord. In the meantime, however, it was feared that nationalist radicalism would rise again following the appointment of Vojislav Seselj, the leader of the ultranationalist Serbian Radical Party, as deputy prime minister . Foreign Ministers decided in the Contact Group to maintain the first restrictive measures. Milosevic was given a new four weeks deadline to avoid further sanctions, and the UN Security Council Resolution 1160, imposing an arms embargo on the FRY, was finally adopted on 31 March. [START_REF]Resolution 1160[END_REF] Tensions continued to be high. There was FRY army activity at the Kosovo border and its police repression intensified. The Contact Group met in Rome on April 29, 'to decide on next steps regarding the increasingly dangerous situation in Kosovo.' [START_REF]Statement on Kosovo[END_REF] Foreign Ministers (excluding Russian) recognized Belgrade's non-compliance to the requirements that they had set out. They decided to freeze the funds of the FRY and Serbian government and threatened to stop new investment in Serbia by 9 May if dialogue was still blocked. The EU rapidly approved the freezing of funds in the common position adopted on the 7 th of May. 9

In a preparation meeting for the G8 Birmingham Summit (London 8-9 May) Foreign Ministers insisted on 'Belgrade's primary responsibility' in the failure to start a dialogue. They also decided (again without Russia) to stop new investment in Serbia. 10 Meanwhile, Kosovo Albanian leader Ibrahim Rugova and his delegation agreed to meet Slobodan Milosevic in Belgrade on 15 May. The dialogue was supposed to begin with no preconditions, and this new move toward dialogue was welcomed by the withdrawal of the EU's new common position from the agenda.

It was a short-lived calm. A new level of violence was reached by widespread Serbian security force arson and artillery attacks on villages in Kosovo. The conflict prompted a growing stream of refugees into northern Albania and threatened regional security and stability. In response, the EU foreign Ministers adopted a third common position on sanctions on 8 June. 11

European governments were increasingly preoccupied by the deteriorating situation on the ground in Kosovo. During the Cardiff European Council (15-16 June) they agreed to adopt further sanctions by imposing a ban on Yugoslav carrier flights between the FRY and the EU. The Council adopted this fourth restrictive measure on 29 June. [START_REF]2111th Council meeting -General Affairs -Luxembourg[END_REF] There were further sanctions imposed on the FRY during the diplomatic phase of the Kosovo crisis including: refused entry in the member states to persons in the FRY acting against the independent media 13 ; an oil embargo [START_REF]Common position of 23[END_REF] ; an extension of visa bans; a freeze of FRY funds; a prohibition of export finance; an extension of the investment ban; and a comprehensive flight ban. [START_REF]Common position of 10 May 1999[END_REF] None of these restrictive measures had any legal effect on EU economic relations with the FRY, as long as they were not implemented by EC regulations. After fast consultations and reactions from the Foreign Ministers and the CFSP bureaucratic machinery, the decisionmaking process slowed down when it entered into the EC arenas.

APPARENT CONTRADICTIONS OF THE EU SANCTION POLICY

A short week was enough to adopt the first EU common position. No more than one meeting was necessary to reach an agreement on the second common position, and the third was also finalized in one meeting after the Council Conclusions. Contrary to common anticipation about diplomatic negotiations, the process was particularly smooth and fast. More difficult talks took place in other arenas, especially in the Contact Group and in the UN. Some EU member states showed little reluctance to follow decisions devised by a 'directory' of the 'big', but there was never a break from the common decision.

Things were completely different at the second stage of the sanction's decision making process. To have a legal impact on economic relations with Serbia, sanctions had to be implemented by the adoption of Community legislation in all areas that fell within European Community competencies. An EC regulation was necessary for each common position, and the second negotiation was far more difficult than the first stage. This is surprising given that there was already unanimity of votes for the common positions and that the EC regulations needed only majority consent. Realistically, however, it took weeks and even months of tireless internal negotiations. As a result the sanctions were deprived of any substance. As a Dutch diplomat noted, 'in Maastricht, we made the provision that if the Union wants use its economic instruments for sanction measure, then there should be political common position first. But there are two decisions. One is the political decision , which comes first, then we have a decision on the sanctions regulation. And if you look at the sanction regulation negotiation then you see that member states that do agree to the political decision first, very often have very different points of view of the actual need for the sanction if they start discussion on the economic measures. There is a huge different of views. We have to negotiate several weeks and very intensely on a piece of paper with very mix arguments, technical arguments, institutional arguments, legal arguments and economic interest arguments. So it's a very complicated field.' [START_REF] Interview | Discord erodes force of Serbia sanctions[END_REF] The Council adopted the first EC regulation on the 11 th of May 1998, more than two months after the Contact Group statement and the Community's common position and two weeks after the new Contact Group statement for further restrictive measures. The fourth regulation on the ban of flights that came into force the on 7 September 1998 was even more difficult. It took 83 days from the initial political decision to agree. The member states who pushed first for fast and tough sanctions were the same to first strongly oppose the adoption of real economic sanctions. The explanation for this rests in part on fears that the sanctions would have adverse consequences for domestic industries. There were complaints from companies 'such as Telecom Italia and Siemens, German textilemakers and governments, including Greece, which was funding construction of a motorway in Serbia.' 17 Greek and Italian Telecom firms had bought 49 percent of the Serb Telecom organism (Srbija), and a Greek consortium had signed two significant co-operation contracts with the Mines of Treptka and Bor in Kosovo. 18 However, this does not explain why the member states waited until the implementation regulation stage of the sanction decision making process to raise those objections. Before turning to this question, I will show how they managed to reduce the restrictive measures to empty shells.

The main strategy of the member states was to use a legal strike. 'Technical limits' [START_REF]Interview with a French high civil servant, general secretariat for interdepartmental coordination (SGCI)[END_REF] were the recurrent issue. First, they contested the community competencies, putting forward restrictive definitions of the Community Treaty -mainly Article 133 (former 113). Second, the binding effect of some parts of the regulation was contested.

A third strategy was to accept only a restricted definition of the scope of sanctions. The wording of the common positions was always very vague and could be clarified in the EC regulation in two opposite ways. For instance, financial sanctions could be limited to a freezing of funds or extended to a suspension of capital flows. The narrow definition adopted in the regulation threw doubt on the effectiveness of sanctions. The Serbian government had, of course, took precautions to transfer its fund outside the EU before the regulation came into force.

Similar problems of interpretation were raised by the notion of 'investments'. An extensive definition could lead to a comprehensive economic embargo and a restrictive definition would result in a lack of economic effects. The Commission proposed that EU companies be forbidden to 'obtain ownership or control, directly or indirectly, of any kind of asset in the Republic of Serbia', including companies, shares, bonds, loans, and intellectual property rights. This provoked a strong hostility among the member states that had tried to water down tough European Commission proposals'. 20 In the end, they only agreed on an interpretation of investment that prohibits the transfer of funds or financial assets to Serbia. The policy was ineffective, given that there was not such type of investment in Serbia.

Finally, they also contested the applicability of the regulation due to international commitments. The most visible example was the UK failure to enforce the European ban on Yugoslav commercial flights. British officials explained that existing air services agreements made between the UK and Yugoslavia in 1959 took precedence over EU law. Therefore the sanction measures could not apply before a one-year notice period year. 21 The economic sanctions against Serbia adopted in 1998 had no visible economic impact. They were considerably watered-down during the negotiations of the EC regulation implementation. What explanations account for this seeming U-turn? Did the member states simply change their minds? On the contrary, the explanation lies in the domestic political arena. Scientific literature on bureaucratic politics has shown for thirty years (Allison 1971) that states are not unitary actors. Foreign policy is the result of compromise, conflict, and confusion between officials with diverse interests and unequal influence. Allison's model is certainly helpful, but like its competitors it presents the difficulty of developing reasoning based on a separation between national and international (or European) level games.

RELEVANT SUB-FIELDS OF THE EU EXTERNAL RELATION FIELD

We could find useful theories offering insights into the preference formation and decision making process in the growing literature on EU public policies and international relations. One of the main contributions to the understanding of EU and international negotiations was the introduction of different levels of analysis. The logic is that policies are not formulated in a single arena but are the consequences of multiple games bargaining. Multi-level game theory and the multi-level governance approach are certainly diverging on many points. Nonetheless, they both share a common interest in the complexity of the EU or international decisional architecture. Negotiations and outcomes are the result of complex linkages between the games, and one must take into account what is going on in one game to understand the actions of the main protagonist in the other.

In his well-known article published in 1988, Robert Putnam proposed a convincing articulation of the link between the domestic level and the international level, which he used to explain the outcomes of multilateral bargaining. He focused on the size of the 'win-set' each state is assumed to have, or in other words the set of potential international agreement acceptable at domestic level (Putnam 1988; Evans, Jacobson and Putnam 1993). This model was expanded to encompass the European level for the explanation of the agriculture policy reform in 1992 (Patterson 1997). The EU is presumed to be a third intermediate level between the national and the international arena. It is argued that change in public policy is the result of simultaneous changes in those interlocking levels. The multi-level game theory has been integrated into neo-realist and intergovernementalist approaches of the European construction. The intergovernmentalist theory could not be entirely reduced to a state-centric approach, as it take into accounts the domestic conflict and 'national interests'. Nonetheless, states representatives are considered to be the decisive actors connecting domestic interest and European affairs. 22 The multi-level governance analysis contests state centric approaches which emphasize 'European level policy-making as the aggregation of domestic interests' (Marks, Hooghe and Blank 1996: 346: see also Kohler Koch 1996). Rather, it is argued that decision-making competencies are shared by state and non state actors -such as supranational institutions and in our case more specifically the European Commission -at different levels as well as subnational actors. Transnational interests could act independently in the European scene, shattering the traditional separation between domestic and international politics. While contesting the prominent and exclusive role of the state, the governance approach of course does not call into question the multi-level analysis. The articulation between multiple arenas is at the heart of this model. Sutcliffe (2000: 291) argues that 'Policy-making responsibility is now shared among a variety of actors at European, national and subnational levels', and attention has been focused on the strategies of alliance between actors of those levels (Ansel, Parsons and Darden 1997).

When distinguishing between the levels of European games, researchers very often focus exclusively on the separation between a European level and a national or domestic level (Moravcsik 1998, Evans, Jacobson and Putnam, 1993). They explore the relation between domestic politics and EU foreign policy (Hill 1996) and the changes in national foreign policy structure caused by the development of the CFSP (Smith 2000). Although there are fundamental differences between multi-level game and multi-level governance theories, both encompass a similar pattern of level division: a vertical territorial level distinction. On the other hand, the theories offer divergent explanations for the articulation between the levels and on the actors involved in both different arenas.

But in the sanction case, such levels of division are little help. The apparent contradiction of the EU sanction policy could not be attributed to the complex relations between member states, EU and international level negotiations, as far as those contradictions appeared between decisions drawn up at the same territorial level. A territorial division of the games could be relevant for the explanation of each decision process (the CFSP common position and the EC Regulation) but not to understand the links and differences between those decisions.

In one recent article, Sarah Collinson (1999) combined the vertical game division of Putnam's theory with the horizontal division of James Rosenau's 'issue-systems' model. This concept allows her to 'steer the analysis to focus on the interaction of overlapping systems and subsystems of political action that form around specific issues or combination of issues' (Collinson 1999: 207). Therefore, attention could be turned to the analysis of 'overlapping and interacting policy arenas within the EU governance system' and the effects on the policy process and the outcomes of this interaction. This distinction of overlapping policy arenas and the analysis of their relations to each other are particularly appropriate in the external relations' field. The next step is to identify the distinctive systems. Collinson suggests two different and tying ways to locate them: one is to focus on the legal and institutional framework; the second is to focus on the issue.

The constitutional structure is certainly the most visible sign of distinction between the external EU policy areas. The Maastricht Treaty is divided into three pillars with important legal distinctions. Commercial and development policy fall under Article 133 (former 113) and Article 310 (former 238), while foreign and security policy is defined by pillar two provisions. In the first pillar, the European Commission has a monopoly of initiative and leads the implementation of decisions often taken under the rule of majority. In the second pillar, member states keep their faculty to initiate the negotiations and to block the decisions in a socalled intergovernmental system. Of course, the decision making process can not be reduced to a legalistic view and we should always be aware of the variation of power in each area. But it seems quite obvious that the first and second pillars are dealing with different issues and therefore are constitutive of two separate issue-systems, including several narrowly defined issue-systems or issue-'subsystems' -such as external commercial policy or development policy (Collinson 1999: 213).

The two systems or subsystems may have the same issue on their agenda (Rosenau 1990) and the way an issue is handled in one system may have repercussions on its handling in the other system. The political use of an economic instrument, as is the case for economic sanctions, is particularly interesting as far as it spreads some light on the autonomy and interdependency of two important EU external sub-fields. The sanctions were at stake at both first and second pillar levels. One similarity between the two pillars was the effect that CFSP decisions (the sanctions common positions) had on EC agenda setting (the necessity to adopt implementing EC regulations). At the same time this sanction process shows the autonomy of the two subfields allowing strong differences in the policy outcomes (ie tough sanctions in the first pillar and very weak sanctions in the second pillar).

Such divergent policy outcomes are generally attributed to differences of constitutional structures. A widely accepted hypothesis about the effect of institutional rules in the Council is that, under the rule of unanimity voting, the common position eventually reached is the lowest common denominator. By contrast, the majority rule has the effect of mitigating the extreme position (Garrett and Tsebelis 1996; Meunier 2000). In the sanction case, the institutional hypothesis does not work. Tough economic sanctions were easily adopted under the rule of unanimity in the CFSP pillar and weak economic sanctions were more difficult to adopt under the majority rule of the EC pillar. Common positions were not the voice of the more conservative EU member states. Greece, for example, was very reluctant but did not threaten to use its veto right in the CFSP pillar. Furthermore EC regulations under qualified majority voting in the first pillar were radically different than the proposal originally made by the Commission, and were limited to the lowest common denominator.

These facts either undermine the link between institutional rule and the outcome of Council decisions, or mean that institutional rules are less decisive than other variables to explain the variation of degree in EU decisions. The second is more plausible explanation. because voting rules do not fully help us understand the EU's decision to reduce the scope of economic sanctions against Serbia. One of the tacit assumptions of an institutional explanation would have been that the national interest of a state would be the same in all areas. But in our case, the representatives of the same states pushed for a fast adoption of tough sanctions when they took part in the negotiation of the CFSP common position. s. However, they were strongly opposed to any substantial sanction when it came to the adoption of EC regulations. . The problem came not precisely from the multiplicity of national interests but from the diversity of sub-fields, where national interest had to be expressed. This distinction is fundamental. The diverging interests of the member states over time can be attributed to the existence of two different sub-fields, one generally referred to as traditional foreign policy and the other as external economic policy. The differentiation is not caused by the institutional structure of the Maastricht Treaty. If both were under the rule of EC pillar they would remain autonomous, just as the common agriculture policy is autonomous from education and culture policy. In this case, the institutional properties of the pillar do not explain the distinction between the spaces of external relations. They are only signs. The pillar structure of the Treaty is, above all, an institutional translation at one time of a pre-existing structural differentiation.

I do not deny that institutional structures have consequences on the negotiation process and partially explain the outcomes. The argument, however, is that we can not attribute policy system differentiation only to the institutional differences. Otherwise, we need to explain the diverging positions of the member states in the two sub-fields by institutional arguments.

One other significant sign of structural change between the CFSP diplomatic sub-field and EC economic relations' sub-field was the mobilization capacity of interests. This change enables us to explain why private or public industrial interests are linked to domestic or transnational constraints during the EC regulation negotiations and not during the CFSP common position negotiations. Within a sub-field, an issue (for example economic sanction) may or may not be an activator for domestic or transnational interests. Strategies of private or public industrial interests, which seek to gain access to the decision-makers, highlight the line of differentiation between the relevant sub-fields of the EU external relations' field. Multiple channels of contact were used simultaneously at the national and the European level. These channels were used mainly when negotiating the EC implementation regulation, because this is where they had the greatest opportunity to exert influence.

EU economic sanction decisions are issues that are simultaneously localized in two EU external spaces: foreign policy and external economic relations. It is essential to identify this horizontal field division in order to explain how it is possible at the same time to adopt both strong and weak measures.

EXPLAINING THE EU SANCTION DECISIONS

Why has the European Union not consistently been able to transform its economic size and weight into a real and effective foreign policy? The question is on everyone lips and it surfaces on the agenda of many intergovernmental conferences for the revision of the European Treaties. The failure of the European foreign policy is generally put down to the institutional framework of its decision making process. Unlike external economic action, the Common Foreign and Security Policy (CFSP) remains in the hands of the member states in the intergovernmental second pillar of the Maastricht Treaty. It is certainly true for European security policy, as it exists in the European Security and Defence Policy (ESDP). However, European foreign policy is less clear. As noted above, the watering-down of sanctions does not originate in the constitutional pillar structures.

The change of sanctions was first and foremost a change in the sub-field. It was not necessarily a change of players, [START_REF]CFSP Counselors participate in the two games, but in the individual capitals there were quite different actors involved[END_REF] or a change of institutions. Rather, differences between the foreign policy sub-field and the external economic relations' sub-field are structural. The positions and power of the actors are different, as are effective means of action, expectations and concerns. In the first space, they negotiate the economic sanctions as a political issue, bearing in mind their impact on the diplomatic arena. In the second space, sanctions are negotiated mainly as an economic issue, considering their impact on trade and financial relations between the EU and the FRY. Political consequences are the priority in the CFSP sub-field, whereas economic consequences are the principal concern in the external economic relations' sub-field.

According to Robert Pape (1997: 95), what policymakers 'most want to know [is] when the strategy of economic sanction can change another state's behaviour without resorting to military force'. The change of state behaviour is a political change, which is to be expected in the diplomatic sub-field. It is not an economic change. The political and economic impact of sanctions is not necessarily connected. An economic sanction could have diplomatic effects without any economic effect. Talks between Milosevic and Rugova began just after the announcement of new economic sanctions and before the adoption of the implementation measure of the previous sanctions. These economic sanctions were a success, but they were not implemented and therefore could not have any economic impact. The success of the policy was purely political, which is acceptable as long as the real economic impact was not directly connected to the political impact. Economic sanctions may have political costs for the target with or without economic costs. However, neither the Contact Group nor the UN Security Council's aims can be discussed exclusively in economic terms. 'Resonant norms' (Drezner 2000: 81), defined as a legitimate vision of action shared by the diplomatic community, also factor into the analysis.

A priority in the foreign relation sub-field is not necessarily a priority in the external economic sub-field. In the classical diplomatic space, the timing is very important. One should react quickly to any event in the international scene. Deadlines are fixed and respecting them gives credibility. To exert presssure on Milosevic, diplomats and Foreign Ministers fixed very short deadlines (9 days to withdraw security forces, etc.). Considering the strong reservations of the Russian Federation, it was a too short a deadline for a UN security resolution. In the absence of consensus the European and American governments decided, each one for their part, to adopt the sanctions. The first EU common position on restrictive measures against the FRY met the deadline. This was in itself a success. According to the former US Foreign Secretary of State Madeline Albright, it enabled the western security community to 'maintain credible pressure on Belgrade to end repression and restore autonomy (in Kosovo).' . 24 Setting a deadline and upholding it signalled diplomatic determination, the effects of which were noticeable during the diplomatic negotiations. The situation in the economic implementation sub-field was different. The interval between the political decision and the economic implementation was far more important. There was pressure to accelerate the negotiation process, but the expectation for fast action could not overcome the defence of economic interests. The hierarchy of priorities was not the same in this sub-field.

The identification of the two autonomous sub-fields is not sufficient to explain why the EC regulation was hard to negotiate when it was easy to adopt the CFSP common position. Given that the preferences of the principle member states were aligned in both sub-fields, why was it so difficult to adopt the EC regulations? Here we should introduce the constitutional variable in the analysis. The move from the second pillar to the first pillar comes with a change of the relative power position in the space. This change lies partly on the redistribution of institutional resources. For the European Commission, the most sensible evolution occurred. Weak and often marginalized in the CFSP sub-field, the Commission has a strong position in the EC sub-field. Its formal monopoly of initiative gives it the means to formulate strong proposals, allowing the Commission to play a major role in the sanction policy. Tough economic sanctions were not acceptable for the representatives of the principal member states, but they had to wait for the official adoption of the Commission proposal. In addition, they needed to find unanimity on all points to overcome the Commission's text.

CONCLUSION

External economic relations is a well established EU competence, while foreign policy is still considered as intergovernmental matters. The use of economic pressure for political goals is one of the most visible overlaps between these areas. Many authors have shown the complexity of the 'hydra of European Foreign Policy' (Hill and Wallace 1996: 2; Holland 1997), but they tend to focus almost exclusively on the states, trying to understand their strategies and interests and whether they act under CFSP or community arenas (adding the European Commission in the second case). In order to understand the role of the EU in international relations and its capacity to lean on its economic power to increase its political weight, it is useful to identify the relevant spaces of the external relations' field. We could move our attention from the interaction between the national and European levels to the relations between political and economic bureaucratic sub-fields.

During 1998, while Foreign Ministers and EU diplomats were engaged on a test of strength with the Yugoslav government, ministries and bureaucrats interested in the EU's economic interest were trying to minimize the economic cost of the retaliation against Milosevic. What appears as a contradiction of the EU foreign policy was only the effect of the differentiation of the sub-fields of the EU external relations' field. Diplomatic and external economic policies could peacefully coexist and develop in different directions. It is only in situations where calls for more consistency are made that, the divergent outcomes of the structurally differentiated EU external relations field becomes a problem. Expectation for consistency is one way of thinking the competence's struggles (between COREPER and Political Comity) that often characterize interpillar relations (Ginsberg 1997). However, this was not the case during the first diplomatic stage of the Kosovo crises. Economic sanctions were an issue that crossed over into the two sub-fields, but the horizontal repercussion was essentially limited to the impulse of the negotiation in the EC external economic space after the adoption of the CFSP common positions. Thus, there was every chance that the actors involved in the two distinctive and relatively autonomous sub-fields of the EU external relations would produce dissonant strategies.

The purpose of this chapter was not to explain why tough sanctions were adopted in the second pillar and empty sanctions in the first pillar (although this could have raised questions about the costs of sanctions in the first pillar where they were probably more specific than in the second pillar) but to explain why the same states could adopt both tough and empty sanctions at the same time. These 'contradictory' outcomes are puzzling as long as we confront activities from the two pillars using a single framework. In other words, we are puzzled if we look at what is happening in the economic external relations field using political diplomatic lenses or when there are crossed field feedbacks -for example, if a diplomat contest the EU political sanction strategy arguing that economic measures have no economical impact. In practice, it is generally not necessary for states to have consistent economic and political interests. Governments can, without difficulty, manage the diversity of their interest as far as they do not cross over into a single issue area. A theoretical approach based on the structural notion of 'fields' offers us the possibility to identify different areas where states (or the EU) are acting out their specific social properties. In our case, the two sub-fields correspond to an institutional particularity of the European Union: the pillar structure. Thus, in order to understand the multi-pillar process, it is important to take into account the differences of logics of actions that lead to specific interest framing.

TABLE 7

 7

		.1: ADOPTION TIMING OF THE FIRST ECONOMIC SANCTIONS AGAINST
	SERBIA
	Political	Nature of	CFSP	Interval EC	Interval	Interval
	decision	economic	Common		Regulation	with	with
		sanctions	Position			Common	Political
						Position	decision
	9 March Embargo on	19 March 10 days 27 April	39 days	49 days
		equipment for
		repression
	29 April Freezing of funds 7 May	8 days 22 June	46 days	54 days
	8 June	Prohibition of new	8 June	-	24 July	46 days	46 days
		Investment
	16 June Ban of flights	29 June	13 days 7 September 70 days	83 days


Enrique Alba

José García-Nieto

Laetitia Jourdan

El-Ghazali Talbi

Gene Selection in Cancer Classification using PSO/SVM and GA/SVM Hybrid Algorithms

In this work we compare the use of a Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA) (both augmented with Support Vector Machines SVM) for the classification of high dimensional Microarray Data. Both algorithms are used for finding small samples of informative genes amongst thousands of them. A SVM classifier with 10fold cross-validation is applied in order to validate and evaluate the provided solutions. A first contribution is to prove that P SOSV M is able to find interesting genes and to provide classification competitive performance. Specifically, a new version of PSO, called Geometric PSO, is empirically evaluated for the first time in this work using a binary representation in Hamming space. In this sense, a comparison of this approach with a new GASV M and also with other existing methods of literature is provided. A second important contribution consists in the actual discovery of new and challenging results on six public datasets identifying significant in the development of a variety of cancers (leukemia, breast, colon, ovarian, prostate, and lung).

I. INTRODUCTION

Microarray technology (DNA microarray) [START_REF] Pease | Light-generated oligonucleotide arrays for rapid dna sequence analysis[END_REF] allows to simultaneously analyze thousands of genes and thus can give important insights about cell's function, since changes in the physiology of an organism are generally associated with changes in gene expression patterns. Several gene expression profiles obtained from tumors such as Leukemia [START_REF] Golub | Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring[END_REF], Colon [START_REF] Alon | Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays[END_REF] and Breast [START_REF] Van 't Veer | Gene expression profiling predicts clinical outcome of breast cancer[END_REF] have been studied and compared to expression profiles of normal tissues. However, expression data are highly redundant and noisy, and most genes are believed to be uninformative with respect to studied classes, as only a fraction of genes may present distinct profiles for different classes of samples. So, tools to deal with these issues are critically important. These tools should learn to robustly identify a subset of informative genes embedded out of a large dataset which is contaminated with highdimensional noise [START_REF] Juliusdottir | Two-phase EA/K-NN for feature selection and classification in cancer microarray datasets[END_REF].

In this context, feature selection is often considered as a necessary preprocess step to analyze these data, as this method can reduce the dimensionality of the datasets and often conducts to better analyses [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF].

Two models of feature selection exist depending on whether the selection is coupled with a learning scheme or not. The first one, filter model, which carries out the feature subset selection and the classification in two separate phases, uses a measure that is simple and fast to compute. Hence, E. Alba and J. García-Nieto are with the Department of Lenguajes y Ciencias de la Computación, Universidad de Málaga, (email: {eat,jnieto}@lcc.uma.es, url http://neo.lcc.uma.es)

L. Jourdan and E-G. Talbi are with the LIFL/INRIA Futurs-Université de Lille 1, Bât M3-Cité Scientifique, (email: {jourdan,talbi}@lifl.fr, url http://www2.lifl.fr/OPAC/) a filter method, is in definition, independent of the learning algorithm used after it. The second one, the wrapper method, which carries out the feature subset selection and classification in the same process, engages a learning algorithm to measure the classification accuracy. From a conceptual point of view, wrapper approaches are clearly advantageous, since the features are selected by optimizing the discriminate power of the finally used induction algorithm.

Feature selection for gene expression analysis in cancer prediction often uses wrapper classification methods [START_REF] Kohavi | The wrapper approach[END_REF] to discriminate a type of tumor, to reduce the number of genes to investigate in case of a new patient, and also to assist in drug discovery and early diagnosis. Several classification algorithms could be used for wrapper methods, such as K-Nearest Neighbor (K-NN) [START_REF] Fix | Discriminatory analysis. nonparametric discrimination: Consistency properties[END_REF] or Support Vector Machines (SVM) [START_REF] Cortes | Support-vector networks[END_REF]. By creating clusters a big reduction of the number of considered genes and an improvement of the classification accuracy can be finally achieved.

The definition of the feature selection problem is this: given a set of features

F = {f 1 , ..., f i , ..., f n }, find a subset F ⊆ F that maximizes a scoring function Θ : Γ → G such that F = argmax G⊂Γ {Θ(G)}, ( 1
)
where Γ is the space of all possible feature subsets of F and G a subset of Γ. The optimal feature selection problem has been shown to be NP-hard [START_REF] Narendra | A branch and bound algorithm for feature subset selection[END_REF]. Therefore, only heuristics approaches are able to deal with large size problems. Recently, such advanced structured methods have been used to explore the huge space of feature subsets, like for example metaheuristics as Evolutionary Algorithms and, specifically, Genetic Algorithms (GAs) [START_REF] Yang | Feature Extraction, Construction and Selection: A Data Mining Perspective[END_REF], [START_REF] Juliusdottir | Two-phase EA/K-NN for feature selection and classification in cancer microarray datasets[END_REF], [START_REF] Huerta | A Hybrid GASVM Approach for Gene Selection and Classification of Microarray Data[END_REF].

In this work, we are interested in gene selection and classification of DNA Microarray data in order to distinguish tumor samples from normal ones. For this purpose, we propose two hybrid models that use metaheuristics and classification techniques. The first one consists of a Particle Swarm Optimization (PSO) [START_REF] Kennedy | Particle Swarm Optimization[END_REF] combined with a SVM approach. PSO is a population based metaheuristic inspired by the social behavior of bird flocking or fish schooling. Specifically, a recent version called Geometric PSO [START_REF] Moraglio | Geometric Particle Swarm Optimization[END_REF] (explained in Section II) has been used in this work. The second model is based on the popular GA using a specialized Size-Oriented Common Feature Crossover Operator (SSOCF) [START_REF] Jourdan | A genetic algorithm for feature selection in data-mining for genetics[END_REF], which keeps useful informative blocks and produces offsprings which have the same distribution than the parents. This model will be also combined with SVM in our approach.

Both proposed approaches are experimentally assessed on six well-known cancer datasets (Leukemia, Colon, Breast, Ovarian [START_REF] Petricoin | Use of proteomic patterns in serum to identify ovarian cancer[END_REF], Prostate [START_REF] Singh | Gene expression correlates of clinical prostate cancer behavior[END_REF] and Lung [START_REF] Gordon | Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma[END_REF]), discovering new and challenging results and identifying specific genes that our work suggests as significants. Performances of proposed PSO and GA algorithms solving the gene extraction problem (using SVM) are compared in this paper. Specifically, we focused in the capacity of the P SO SV M combination in order to provide considerable performance in this matter. In this sense, comparisons with several state of art methods show competitive results according to the conventional criteria.

The outline of this work as follows. We review the PSO and the SVM techniques in order to introduce our P SO SV M hybrid model in Section II. In Section III, the six microarray datasets used in this study are described. Experimental results are presented in Section IV, including biological descriptions of several obtained genes. Finally, we summarize our work and present some conclusions and possible future work in Section V.

II. GENE SELECTION AND CLASSIFICATION BY P SO SV M

In this section, we describe the hybrid P SO SV M approach for gene selection and classification of Microarray data. The PSO algorithm is designed for obtaining gene subsets as solutions in order to reduce the high number of genes to be later classified. The SVM classifier is used whenever the fitness evaluation of a tentative gene subset is required.

A. Particle Swarm Optimization

Particle Swarm Optimization was first proposed by Kennedy and Eberhart in 1995 [START_REF] Kennedy | Particle Swarm Optimization[END_REF]. PSO is a population based evolutionary algorithm inspired in the social behavior of bird flocking or fish schooling. In the description of PSO, the swarm is made up of a certain number of particles (similar to population of individuals in EAs). At each iteration, all the particles move in the problem space to find the global optima. Each particle has a current position vector and a velocity vector for directing its movement.

v k+1 i = ω • v k i + ϕ 1 • rnd 1 • (pBest i -x k i ) + ϕ 2 • rnd 2 • (g i -x k i ) (2)
x k+1 i = x k i + v k+1 i ( 3
)
Equations 2 and 3 describe the velocity and position update of a given particle i at a certain iteration k. Equation 2 calculates a new velocity v i for each particle (potential solution) based on its previous velocity, the particle's location at which the best fitness so far has been found pBest i , and the population global (or local neighborhood, in the neighborhood version of the algorithm) location at which the best fitness so far has been achieved g i . Individual and social weight are represented by means of ϕ 1 and ϕ 2 factors respectively. Finally, rnd 1 and rnd 2 are random numbers in range {0, 1}, and ω represents the inertia weight factor. Equation 3 updates each particle's position x i in solution space.

B. The SVM Classifier

Support Vector Machines, a technique derived from statistical learning theory, is used to classify points by assigning them to one of two disjoint half spaces [START_REF] Cortes | Support-vector networks[END_REF]. So, SVM performs mainly a (binary) 2-class classification. For linearly separable data, SVM obtains the hyperplane which maximizes the margin (distance) between the training samples and the class boundary. For non linearly separable cases, samples are mapped to a high dimensional space where such a separating hyperplane can be found. The assignment is carried out by means of a mechanism called the kernel function.

SVM is widely used in the domain of cancer studies, protein identification and specially in Microarray data [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF], [START_REF] Huerta | A Hybrid GASVM Approach for Gene Selection and Classification of Microarray Data[END_REF], [START_REF] Mukherjee | Classifying Microarray data using Support Vector Machines[END_REF]. Unfortunately, in many bioinformatics problems the number of features is significantly larger than the number of samples. For this reason, tools for decreasing the number of features in order to improve the classification or to help to identify interesting features (genes) in noisy environments are necessary. In addition, SVM can treat data with a large number of genes, but it has been shown that its performance is increased by reducing the number of genes [START_REF] Furey | Support vector machines classification and validation of cancer tissue samples using microarray expression data[END_REF]. The hybrid PSO and hybrid GA approaches next proposed contribute notably in this sense.

C. The Hybrid P SO SV M Approach

In order to offer a basic idea of the operation of our P SO SV M approach, in Figure 1, we can observe a simple scheme of how features are extracted from the initial microarray dataset and how the resulted subset is evaluated.

In a first phase, the metaheuristic algorithm involved, PSO in this case, provides a binary encoded particle 1 where each bit 2 represents a gene. If a bit is 1, it means that this gene is kept in the subset and 0 indicates that the gene is not included in the subset. Therefore, the particle length is equal to the number of genes in the initial microarray dataset.

The original PSO was initially developed for continuous optimization problems. However, lots of practical engineering problems are formulated as combinatorial optimization problems and specifically as binary decisions. Several binary versions of PSO can be found in present literature [START_REF] Kennedy | A Discrete Binary Version of the Particle Swarm Algorithm[END_REF], [START_REF] Clerc | Binary Particle Swarm Optimisers: Toolbox, Derivations, and Mathematical Insights[END_REF]. Nevertheless, these versions consist on ad hoc adaptations from the original PSO and therefore their performances are usually improvable.

With the aim of facing the gene selection problem, an innovative version of PSO, based on the geometric framework presented in [START_REF] Moraglio | Geometric Particle Swarm Optimization[END_REF], has been developed in this work. This version, called Geometric Particle Swarm Optimization (GPSO), enables to us to generalize PSO to virtually any solution representation in a natural and straightforward way. This property was demonstrated for the cases of Euclidean, Manhattan and Hamming spaces in the referenced work. Even a recently appeared work [START_REF] Moraglio | Geometric particle swarm optimization for the sudoku puzzle[END_REF], uses the GPSO for solving the Sudoku Puzzle by means of permutations Fig. 1. A simple scheme of how features (genes) are selected out from the original microarray dataset using a particle with binary encoding. In a second phase, the resulted subset is evaluated by means of a SVM classifier and 10-fold cross validation to obtain the fitness value (accuracy) of such particle.

representation. Since the gene selection problem has been represented by binary way, specific operators for Hamming space were used in the PSO described here.

D. Geometric Particle Swarm Optimization

In this version, the location of each particle i is represented as vector x i = x i1 , x i2 , ..., x iN taking each bit x ij (with j in {1, N}) binary values 0 or 1. The key issue of the GPSO is the concept of particle movement. In this approach, instead of the notion of velocity added to the position, a three-parent mask-based crossover (3PMBCX) operator is applied to each particle in order to "move" it. According to the definition of 3PMBCX [START_REF] Moraglio | Geometric Particle Swarm Optimization[END_REF], given three parents a, b and c in {0, 1} n , generate randomly a crossover mask of length n with symbols from the alphabet {a, b, c}. Build the offspring filling each element with the bit from the parent appearing in the crossover mask at the position.

The pseudocode of the GPSO algorithm for Hamming spaces is illustrated in Algorithm 1. For a given particle i, three parents take part in the 3PMBCX operator (line 13): the current position x i , the social best position g i and the historical best position found h i (of this particle). The weight values w1, w2 and w3 indicate for each element in the crossover mask the probability of having values from the parents x i , g i or h i respectively. These weight values associated to each parent represent the inertia value of the current position (w1), the social influence of the global/local best position (w2) and the individual influence of the historical best position found (w3). A constriction of the geometric crossover forces w1, w2 and w3 to be non-negative and add up to one.

In summary, the GPSO developed in this study operates as follows: In a first phase of the pseudocode, the initialization of particles are carried out by means of the SwarmInitialization() function (Line 1). This special initialization method (used also in our GA approach) was adapted to gene selection as follows. The swarm (population) was divided into four subsets of particles (chromosomes) initialized in different ways depending on the number of features in each particle. That is, 10% of particles were initialized with N (prefixed value) selected genes (1s) located randomly. Another 20% of particles were initialized with 2N genes, 30% with 3N genes and finally, the rest of particles (40%) were initialized randomly and 50% of the genes were turned on. In these experiments N will be equal to 4. In Algorithm 1 Pseudocode of the GPSO for Hamming space.

1: S ← SwarmInitialization() 2: while not stop condition do 3:

for each particle x i of the swarm S do 4:

evaluate(x i ) 5:

if fitness(x i ) is better than fitness(h i ) then 6:

h i ← x i 7:
end if 8:

if fitness(h i ) is better than fitness(g i ) then 9:

g i ← h i 10:
end if 11:

end for 12:

for each particle x i of the swarm S do 13:

x i ← 3P MBCX((x i , w 1 ), (g i , w 2 ), (h i , w 3 )) 14:

mutate(x i ) 15:

end for 16: end while 17: Output: best solution found a second phase, after the evaluation of particles (line 4), historical and social position are updated (lines 5 to 10). Finally, particles are "moved" by means of the 3PMBCX operator (line 13). In addition, with a probability of 10%, a simple bit-mutation operator (line 14) is applied in order to avoid the early convergence. This process is repeated until reach the stop condition fixed to a certain number of evolutions.

E. Evaluation Function

Since the position of a particle x i represents a gene subset, the evaluation of each particle is carried out by means of the SVM classifier to assess the quality of the represented gene subset. The fitness of a particle x i is calculated applying a 10fold Cross Validation (10FCV) method to calculate the rate of correct classification (accuracy) of a SVM trained with this gene subset. In 10FCV, the data set is divided into 10 subsets. Each time, one of the 10 subsets is used as the test set and the other 9 subsets are put together to form the training set. Then the average error across all 10 trials is computed. The complete fitness function is described in Equation 4.

f itness(x) = α • (100/accuracy) + β • #features, (4)
where α and β are weight values set to 0.75 and 0.25 respectively in order to control that the accuracy value takes priority over the subset size, since high accuracies are preferred when guiding the search process. The objective here consists of maximizing the accuracy and minimizing the number of genes (#features). For convenience (only minimization of fitness) the first factor is presented as (100/accuracy).

III. DATA SETS

IV. EXPERIMENTAL RESULTS AND COMPARISONS

For our P SO SV M approach, the PSO was implemented in C++ following the skeleton architecture of the MALLBA [START_REF] Alba | Mallba: A Library of Skeletons for Combinatorial Optimisation[END_REF] library. For the GA SV M approach the GA was implemented in C++ using the ParadisEO [START_REF] Cahon | Paradiseo: A framework for parallel and distributed metaheuristics[END_REF] Framework. The GA implements a generational evolution strategy (offspring replacement with elitism) and uses the following operators: deterministic tournament selection, SSOCF crossover, and uniform mutation. The SVM classifier used in both approaches is based on the LIBSVM [START_REF] Chang | LIBSVM: A library for support vector machines[END_REF] library. For the SVM confi-guration, the same parameters were used in PSO and GA algorithms and the Kernel function was configured as Linear. The fitness function used in GA SV M is the same one (described in Section II) as in P SO SV M .

All experiments were carried out using a PC with Linux O.S (Suse 9.0 with kernel 2.4.19) and a Pentium IV 2.8GHz processor, with 512MB of RAM. P SO SV M and GA SV M algorithms on six cancer related microarray datasets were independent executed 10 times over each dataset, in order to have statistically meaningful conclusions as both algorithms are stochastics.

A. Parameters Settings

The parameters used in our PSO and GA algorithms are shown in Table I. These parameter were selected after several test evaluations of each algorithm and dataset instance until reach the best configuration in terms of the quality of solutions and the computational effort.

B. Discussion and Analysis

Several observations can made based on the above experiments, so we tackle the analysis of results focusing on the performance and robustness of our algorithms, as well as the quality of the obtained solutions providing a biological description of most significant ones.

1) Performance Analysis: From the point of view of the performance, both algorithms obtain in a few iterations acceptable results in gene selection, providing reduced subsets with high classification rates. However, the behavior is slightly different. Figure 2 shows a graphical evolution, in terms of the average of the fitness value, of a typical execution of P SO SV M and GA SV M . It is noticeable that in few iterations (4 or 5) the average of fitness decrease quickly and then stop in similar solutions. The large diversity of solutions provided in the initialization method (Section II-B) provokes fast of good solutions and the early convergence of both methods. Although the GA SV M generally obtains lower average than P SO SV M , whose solutions have in turn higher diversity.

Results for all the datasets are shown in Table II. Columns 2 and 3 contain the average of the best solutions obtained in 10 independent executions of P SO SV M and GA SV M respectively. Six state of the art methods from literature are presented in columns 4 to 10 in order to show how our proposals actually push forward the research in this area. Cells in -haven't values to our knowledge. Standard criteria are used to compare the results: the classification accuracy in terms of the rate of correct classification (first value in every table cell) and the number of selected genes (the value in parenthesis). In this comparison, we can observe that all solutions provided by our P SO SV M and GA SV M algorithms present a classification rate higher than 86%, and subsets with four and less than four selected genes are common. We outperform all the existing results (to our knowledge) but one case [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF] presents a smaller subset of 2 genes. We suspect that the initialization method used in our work helps the performance of the algorithms significantly, finding small subsets with a high classification accuracy. PSO with SVM algorithm was also used in [START_REF] Shen | A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and classification[END_REF] (PSO/SVM from now on), although showing several differences with respect to our P SO SV M . In the first place, in PSO/SVM the binary values of each particle are chosen at each iteration by means of a decision function based on threshold parameters, whereas in P SO SV M , binary particles evolve following a completely different mechanism, that is, 3PMBCX crossover and mutation operators (Subsection II-D). In second place, during the evaluation phase, a leave-half-out cross-validation method was used in PSO/SVM whereas the P SO SV M validates the selected subsets by means of 10 k fold cross-validation.

If we compare our GPSO and GA metaheuristics combined with SVM similar results are found. In general, the GA approach obtain better best solutions (Hits) although the best classification was provided by GP SO SV M for the Colon tumor dataset (100% accuracy and 2 genes in Table IV). From the point of view of the accuracy average in all independent runs, the GPSO obtain a better performance although the difference with regard to the GA (as shows Fig. 3) is insignificant.

2) Algorithm Robustness: One of the most important criteria in evaluating any proposed algorithm is the quality of the algorithm and its ability to generate similar (identical) outcomes when executed several times. This factor is very important for metaheuristics which is the case in this work. To examine the robustness of the two proposed approaches, in some instances, in all ten runs both algorithms manages to find the same answer or similar ones (not identical). However, it is worthwhile mentioning that the total accuracy and number of selected features in all the cases did not deviate from each other by more than 5.5. Table III 3) Brief Biological Analysis of Selected Genes: Finally, a summary of the best subsets of genes found for each dataset is shown in Table IV. All subset of genes which reported close to 100% test accuracy and the minimum number of genes. It is remarkable that apparently (to our knowledge) several discovered genes that has not been seen in any past studies. In this sense, we can provide a brief biological description of some of the most frequently obtained genes since they are currently used in the design of drugs and cancers treatment. Some of which are listed below:

• Gene L12052 at is "CAMP phosphodiesterase mRNA, 3' end" which is used in drugs like Anagrelide or Milrinone. Specifically the Anagrelide is used for the treatment of essential thrombocytosis, and it was proved to be effective in treating patients with certain kinds of leukemia such as chronic myeloid leukemia [START_REF] Silver | Anagrelide is effective in treating patients with hydroxyurea-resistant thrombocytosis in patients with chronic myeloid leukemia[END_REF]. This gene belongs to a set of 3 genes (reported from leukemia dataset in Table IV) with 100% accuracy selected by the P SO SV M . • Gene AB022847 is a "Solute carrier family 6 (neu- Fig. 3. Accuracy obtained by P SO SV M and GA SV M in each independent run. Legends specify the datasets with the number of features in parenthesis.

rotransmitter transporter, noradrenalin), member 2" located in plasma membrane. Current drugs like Radaxafine, Amphetamine or Venlafaxine are associated with this gene. Specifically, Venlafaxine is a prescription antidepressant first introduced by Wyeth in 1993. It is specifically used in management of hot flashes in survivors of breast cancer [START_REF] Loprinzi | Venlafaxine in management of hot flashes in survivors of breast cancer: a randomised controlled trial[END_REF]. This gene belongs to a set of 3 genes (reported from breast dataset in Table IV) with 95.8763% accuracy selected by the P SO SV M . • Gene 36245 at is "5-hydroxytryptamine (serotonin) receptor 2B" located in human plasma membrane. There are several drugs where this gene is used like Risperidone, Blonanserin and Mirtazapine. Some studies consider Mirtazapine as the first-choice agent for anxiety and depression after lung transplantation. This gene belongs to a set of 4 genes (reported from lung dataset in Table IV) with 100% accuracy selected by the GA SV M .

V. CONCLUSIONS AND FUTURE WORK

In this work, two hybrid techniques for gene selection and classification of high dimensional DNA Microarray data were presented and compared. These techniques are based on different metaheuristic algorithms such as GPSO and GA used for feature selection using the SVM classifier to identify potentially good gene subsets. Specifically, the Geometric PSO algorithm for Hamming space was used to solve a real problem (gene selection in this case) for the first time (to our knowledge). In addition, genes selected are validated by an accurate 10-fold cross validation method to improve the actual classification.

Both approaches (P SO SV M and GA SV M ) were experimentally assessed on six well-known cancer datasets discovering new and challenging results, and identifying specific genes that our work suggests as significant ones. In this sense, comparisons with several state of art methods show competitive results according to standard evaluation. Results of 100% classification rate and few genes per subset (3 and 4) are obtained in most of our executions. The use of an adapted initialization method has shown a great influence on the performance of proposed algorithms, since it introduces an early set of acceptable solutions in their evolution process.

Continuing the line of this work, we are interested in developing and testing several combinations of other metaheuristics with classification methods in order to discover new and better subsets of genes using specific Microarray datasets. In this sense, the utilization of multiobjective approaches could contribute notably in gene subset selection.

Fig. 2 .

 2 Fig.2. Evolution of the average fitness (AVG Fitness) in a typical execution of P SO SV M and GA SV M approaches using the Leukemia dataset.

TABLE II COMPARISON

 II OF RELEVANT WORKS ON CANCER CLASSIFICATION WITH PROPOSED MODELS GA SV M AND P SO SV M . IN BOLD WE MARK THE MOST ACCURATE RESULTS. CELLS WITHOUT KNOWN VALUE (TO US) ARE MARKED WITH -CHARACTER

	Dataset	P SOSV M	GASV M	[5]	[6]	[12]	[27]	[28]	[29]	[30]
	Luekemia	97.38(3)	97.27(4)	-	100(2)	100(25)	100(4)	87.55(4)	-	-
	Breast	86.35(4)	95.86(4)	-	-	-	-	79.38(67)	-	-
	Colon	100(2)	100(3)	94.12(37)	98.0(4)	99.41(10)	97.0(7)	93.55(4)	85.48(-)	94.00(4)
	Lung	99.00(4)	99.49(4)	-	-	-	-	98.34(6)	-	-
	Ovarian	99.44(4)	98.83(4)	-	-	-	-	-	99.21(75)	-
	Prostate	98.66(4)	98.65(4)	88.88(20)	-	-	-	-	-	-

  shows the result of running the GA SV M and P SO SV M algorithms in terms of statistical results, reporting the Best solution found, Mean and Standard Deviation of ten independent runs.

TABLE III COMPARISON

 III IN TERMS OF STATISTICAL RESULTS OF THE GA SV M AND P SO SV M APPROACHES. THE Best SOLUTION FOUND, Mean AND Standard Deviation OF 10 INDEPENDENT RUNS WERE REPORTED

	Dataset	Best	P SOSV M Mean	Std Dev.	Best	GASV M Mean	Std Dev.
	Leukemia	100(3)	97.38(3)	3.80	100(4)	97.27(4)	3.82
	Breast	90.72(4)	86.35(4)	4.11	100(4)	95.86(4)	5.33
	Colon	100(2)	100(2)	0.0000	100(3)	100(3)	0.0000
	Lung	99.44(4)	99.00(4)	0.50	100(4)	99.49(4)	0.41
	Ovarian	100(4)	99.44(4)	0.38	100(4)	98.83(4)	3.18
	Prostate	100(4)	98.66(4)	1.14	100(4)	98.65(4)	3.24

chromosome in GA and solution (S) in Figure1

ACKNOWLEDGMENT

Authors acknowledge founds from the INRIA-PERFOM 3+3 Mediterranean project. E. Alba and J. García-Nieto acknowledge founds from the Spanish Ministry of Education and European FEDER under contract TIN2005-08818-C04-01 (the OPLINK project, http://oplink.lcc.uma.es).