Adaptation in evolutionary computation: a survey

Adaptation of parameters and operators is one of the most important and promising areas of research in evolutionary computation; it tunes the algorithm to the problem while solving the problem. In this paper we develop a classification of adaptation on the basis of the mechanisms used, and the level at which adaptation operates within the evolutionary algorithm. The classification covers all forms of adaptation in evolutionary computation and suggests further research.

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

[2]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[3]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[4]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

[5]  F. Landy.  What have you done for me Lately , 1982 .

[6]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  C. G. Shaefer,et al.  The ARGOT Strategy: Adaptive Representation Genetic Optimizer Technique , 1987, ICGA.

[8]  J. David Schaffer,et al.  An Adaptive Crossover Distribution Mechanism for Genetic Algorithms , 1987, ICGA.

[9]  Terence C. Fogarty,et al.  Varying the Probability of Mutation in the Genetic Algorithm , 1989, ICGA.

[10]  Lawrence Davis,et al.  Adapting Operator Probabilities in Genetic Algorithms , 1989, ICGA.

[11]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[12]  Reinhard Männer,et al.  Towards an Optimal Mutation Probability for Genetic Algorithms , 1990, PPSN.

[13]  David B. Fogel,et al.  Meta-evolutionary programming , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[14]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[15]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: Some Asymptotical Results from the (1,+ )-Theory , 1993, Evolutionary Computation.

[16]  Atidel B. Hadj-Alouane,et al.  A dual genetic algorithm for bounded integer programs James C. Bean, Atidel Ben Hadj-Alouane. , 1993 .

[17]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[18]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: The (, )-Theory , 1994, Evolutionary Computation.

[19]  Tony White,et al.  Adaptive Crossover Using Automata , 1994, PPSN.

[20]  Zbigniew Michalewicz,et al.  Evolutionary optimization of constrained problems , 1994 .

[21]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[22]  Christopher R. Houck,et al.  On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[23]  Peter J. Angeline,et al.  Adaptive and Self-adaptive Evolutionary Computations , 1995 .

[24]  William M. Spears,et al.  Adapting Crossover in Evolutionary Algorithms , 1995, Evolutionary Programming.

[25]  Bryant A. Julstrom,et al.  What Have You Done for Me Lately? Adapting Operator Probabilities in a Steady-State Genetic Algorithm , 1995, ICGA.

[26]  Robert G. Reynolds,et al.  Adapting Crossover in Evolutionary Algorithms , 1995 .

[27]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[28]  David B. Fogel,et al.  An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines , 1995, Evolutionary Programming.

[29]  R. Hinterding,et al.  Gaussian mutation and self-adaption for numeric genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[30]  A. E. Eiben,et al.  Self-adaptivity for constraint satisfaction: learning penalty functions , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[31]  Peter Ross,et al.  Cost Based Operator Rate Adaption: An Investigation , 1996, PPSN.

[32]  Zbigniew Michalewicz,et al.  Evolutionary Planner/Navigator: operator performance and self-tuning , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[33]  Xin Yao,et al.  Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared , 1996, PPSN.

[34]  Joanna Lis,et al.  Parallel genetic algorithm with the dynamic control parameter , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[35]  Heinz Mühlenbein,et al.  Adaptation of population sizes by competing subpopulations , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[36]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[37]  Jim Smith,et al.  Self adaptation of mutation rates in a steady state genetic algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[38]  Zbigniew Michalewicz,et al.  Self-Adaptive Genetic Algorithm for Numeric Functions , 1996, PPSN.

[39]  A. Eiben,et al.  Solving 3-SAT by GAs adapting constraint weights , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[40]  R. Hinterding.  Self-adaptation using multi-chromosomes , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[41]  Jano I. van Hemert,et al.  Graph Coloring with Adaptive Evolutionary Algorithms , 1998, J. Heuristics.