Evolutionary Computation and Heuristics

Evolutionary computation techniques constitute an important category of heuristic search. Any evolutionary algorithm applied to a particular problem must address the issue of genetic representation of solutions to the problem and genetic operators that would alter the genetic composition of offspring during the reproduction process. However, additional heuristics should be incorporated in the algorithm as well; these heuristic rules provide guidelines for evaluating unfeasible and feasible individuals. This paper surveys such heuristics for discrete and continuous domains and discusses their merits and drawbacks.

[1]  Marc Schoenauer,et al.  Constrained GA Optimization , 1993, ICGA.

[2]  T. M. English Proceedings of the third annual conference on evolutionary programming: A.V. Sebald and L.J. Fogel, River Edge, NJ: World Scientific, ISBN 981-02-1810-9, 371 pages, hardbound, $78 , 1995 .

[3]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[4]  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 .

[5]  Zbigniew Michalewicz,et al.  Using Cultural Algorithms for Constraint Handling in GENOCOP , 1995, Evolutionary Programming.

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

[7]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

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

[9]  Abdollah Homaifar,et al.  Constrained Optimization Via Genetic Algorithms , 1994, Simul..

[10]  L. C. Stayton,et al.  On the effectiveness of crossover in simulated evolutionary optimization. , 1994, Bio Systems.

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

[12]  Klaus Schittkowski,et al.  More test examples for nonlinear programming codes , 1981 .

[13]  Alice E. Smith,et al.  Genetic Optimization Using A Penalty Function , 1993, ICGA.

[14]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[15]  Jack Sklansky,et al.  Constrained Genetic Optimization via Dynarnic Reward-Penalty Balancing and Its Use in Pattern Recognition , 1989, ICGA.

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

[17]  James C. Bean,et al.  A Genetic Algorithm for the Multiple-Choice Integer Program , 1997, Oper. Res..

[18]  L. Darrell Whitley,et al.  Lamarckian Evolution, The Baldwin Effect and Function Optimization , 1994, PPSN.

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

[20]  Jan Paredis,et al.  Co-evolutionary Constraint Satisfaction , 1994, PPSN.

[21]  Gunar E. Liepins,et al.  Some Guidelines for Genetic Algorithms with Penalty Functions , 1989, ICGA.

[22]  Michael M. Skolnick,et al.  Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints , 1993, ICGA.

[23]  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.

[24]  Lashon B. Booker,et al.  Proceedings of the fourth international conference on Genetic algorithms , 1991 .

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

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

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

[28]  Zbigniew Michalewicz,et al.  Handling Constraints in Genetic Algorithms , 1991, ICGA.