Multi-objective optimization by genetic algorithms: a review

The paper reviews several genetic algorithm (GA) approaches to multi objective optimization problems (MOPs). The keynote point of GAs to MOPs is designing efficient selection/reproduction operators so that a variety of Pareto optimal solutions are generated. From this viewpoint, the paper reviews several devices proposed for multi objective optimization by GAs such as the parallel selection method, the Pareto based ranking, and the fitness sharing. Characteristics of these approaches have been confirmed through computational experiments with a simple example. Moreover, two practical applications of the GA approaches to MOPs are introduced briefly.

[1]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[2]  John R. Koza,et al.  Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm , 1990, PPSN.

[3]  Karl Sims,et al.  Artificial evolution for computer graphics , 1991, SIGGRAPH.

[4]  Hisashi Tamaki,et al.  Generation of a Set of Pareto-Optimal Solutions by Genetic Algorithms , 1995 .

[5]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[6]  Michael P. Fourman,et al.  Compaction of Symbolic Layout Using Genetic Algorithms , 1985, ICGA.

[7]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[8]  Thomas G. Dietterich,et al.  Learning with Many Irrelevant Features , 1991, AAAI.

[9]  N. Mori,et al.  A thermodynamical selection rule for the genetic algorithm , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[10]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[11]  Thomas G. Dietterich,et al.  Learning Boolean Concepts in the Presence of Many Irrelevant Features , 1994, Artif. Intell..

[12]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[13]  A. Osyczka,et al.  A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm , 1995 .

[14]  Hajime Kita,et al.  Multi-Objective Optimization by Means of the Thermodynamical Genetic Algorithm , 1996, PPSN.