The compact genetic algorithm

Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact GA is guided by a proper understanding of the role of the GA's parameters and operators. The paper clearly illustrates the mapping of the simple GA's parameters into those of an equivalent compact GA. Computer simulations compare both algorithms in terms of solution quality and speed. Finally, this work raises important questions about the use of information in a genetic algorithm, and its ramifications show us a direction that can lead to the design of more efficient GAs.

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

[2]  S. Baluja,et al.  Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .

[3]  David E. Goldberg,et al.  SEARCH, Blackbox Optimization, And Sample Complexity , 1996, FOGA.

[4]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[5]  D. E. Goldberg,et al.  Simple Genetic Algorithms and the Minimal, Deceptive Problem , 1987 .

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

[7]  G. Harik Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .

[8]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[9]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[10]  David E. Goldberg,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1999, Evolutionary Computation.

[11]  Paul A. Viola,et al.  MIMIC: Finding Optima by Estimating Probability Densities , 1996, NIPS.

[12]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[13]  Gilbert Syswerda,et al.  Simulated Crossover in Genetic Algorithms , 1992, FOGA.

[14]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[15]  M. Pelikán,et al.  The Bivariate Marginal Distribution Algorithm , 1999 .

[16]  Larry J. Eshelman,et al.  Crossover's Niche , 1993, International Conference on Genetic Algorithms.

[17]  D. Ackley A connectionist machine for genetic hillclimbing , 1987 .

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

[19]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.