Compressed-Objective Genetic Algorithm Kuntinee Maneeratana 1 , Kittipong Boonlong 1 , and Nachol Chaiyaratana 2 1 Department of Mechanical Engineering, Chulalongkorn University Phaya Thai Road, Pathum Wan, Bangkok 10330, Thailand kuntinee.m@chula.ac.th, kittipong toy@yahoo.com 2 Research and Development Center for Intelligent Systems, King Mongkut’s Institute of Technology North Bangkok 1518 Piboolsongkram Road, Bangsue, Bangkok 10800, Thailand nchl@kmitnb.ac.th Abstract. A strategy for solving an optimisation problem with a large number of objectives by transforming the original objective vector into a two-objective vector during survival selection is presented. The trans- formed objectives, referred to as preference objectives, consist of a win- ning score and a vicinity index. The winning score, a maximisation cri- terion, describes the difference of the number of superior and inferior objectives between two solutions. The minimisation vicinity index de- scribes the level of solution clustering around a search location, particu- larly the best value of each individual objective, is used to encourage the results to spread throughout the Pareto front. With this strategy, a new multi-objective algorithm, the compressed-objective genetic algorithm (COGA), is introduced. COGA is subsequently benchmarked against a non-dominated sorting genetic algorithm II (NSGA-II) and an improved strength Pareto genetic algorithm (SPEA-II) in six scalable DTLZ bench- mark problems with three to six objectives. The results reveal that the proposed strategy plays a crucial role in the generation of a superior so- lution set compared to the other two techniques in terms of the solution set coverage and the closeness to the true Pareto front. Furthermore, the spacing of COGA solutions is very similar to that of SPEA-II solutions. Overall, the functionality of the multi-objective evolutionary algorithm (MOEA) with preference objectives is effectively demonstrated. 1 Introduction Various techniques have been proposed for solving multi-objective problems. Among these techniques, the genetic algorithm has been established as one of the most widely used methods [1,2,3,4,5,6,7,8]. Due to the parallel search nature of the algorithm, the approximation of multiple optimal solutions—the Pareto optimal solutions, comprising of non-dominated individuals—can be effectively executed. The performance of the algorithm always degrades as the search space or problem size gets bigger. As an increase in the number of conflicting objectives also significantly raises the difficulty level [9], the non-dominated solutions may deviate from the true Pareto front and effects the coverage of the Pareto front by the solutions. T.P. Runarsson et al. (Eds.): PPSN IX, LNCS 4193, pp. 473–482, 2006. c  Springer-Verlag Berlin Heidelberg 2006