Selective Pressure in Evolutionary Algorithms: A Characterization of Selection Mechanisms

Due to its independence of the actual search space and its impact on the exploration-exploitation tradeoff, selection is an important operator in any kind of evolutionary algorithm. All important selection operators are discussed and quantitatively compared with respect to their selective pressure. The comparison clarifies that only a few really different and useful selection operators exist: proportional selection (in combination with a scaling method), linear ranking, tournament selection, and (/spl mu/,/spl lambda/)-selection (respectively (/spl mu/+/spl lambda/)-selection). Their selective pressure increases in the order as they are listed here. The theoretical results are confirmed by an experimental investigation using a genetic algorithm with different selection methods on a simple unimodal objective function. >

[1]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[2]  David B. Fogel,et al.  CONTINUOUS EVOLUTIONARY PROGRAMMING: ANALYSIS AND EXPERIMENTS , 1995 .

[3]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[4]  Bruce Tidor,et al.  An Analysis of Selection Procedures with Particular Attention Paid to Proportional and Boltzmann Selection , 1993, International Conference on Genetic Algorithms.

[5]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[6]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[7]  Donald E. Brown,et al.  Fast generic selection of features for neural network classifiers , 1992, IEEE Trans. Neural Networks.

[8]  Thomas Bäck,et al.  Extended Selection Mechanisms in Genetic Algorithms , 1991, ICGA.

[9]  C. L. Liu,et al.  Introduction to Combinatorial Mathematics. , 1971 .

[10]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[11]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[12]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Hans-Paul Schwefel.  Natural evolution and collective optimum-seeking , 1992 .

[16]  John J. Grefenstette,et al.  How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.

[17]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[18]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[19]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..