Compressed introns in a linkage learning genetic algorithm

Over the last 10 years, many efforts have been made to design a competent genetic algorithm. This paper revisits and extends the latest of such efforts-the linkage learning genetic algorithm. Specifically, it introduces an efficient mechanism for representing the non-coding material. Recent investigations suggest that this new method is crucial for solving a large class of hard optimization problems.

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

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

[3]  James R. Levenick.  Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology , 1991, ICGA.

[4]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[5]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[6]  Kalyanmoy Deb,et al.  RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms , 1993, ICGA.

[7]  Dirk Thierens,et al.  Mixing in Genetic Algorithms , 1993, ICGA.

[8]  Annie S. Wu,et al.  Empirical Studies of the Genetic Algorithm with Noncoding Segments , 1995, Evolutionary Computation.

[9]  James R. Levenick.  Metabits: Generic Endogenous Crossover Control , 1995, ICGA.

[10]  David E. Goldberg,et al.  Learning Linkage , 1996, FOGA.

[11]  Hillol Kargupta,et al.  The Gene Expression Messy Genetic Algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[12]  E. Cantu-Paz,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1997, Evolutionary Computation.

[13]  G. Harik.  Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms , 1997 .

[14]  D. Goldberg,et al.  Domino convergence, drift, and the temporal-salience structure of problems , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).