From Recombination of Genes to the Estimation of Distributions I. Binary Parameters

The Breeder Genetic Algorithm (BGA) is based on the equation for the response to selection. In order to use this equation for prediction, the variance of the fitness of the population has to be estimated. For the usual sexual recombination the computation can be difficult. In this paper we shortly state the problem and investigate several modifications of sexual recombination. The first method is gene pool recombination, which leads to marginal distribution algorithms. In the last part of the paper we discuss more sophisticated methods, based on estimating the distribution of promising points.

[1]  R. B. Robbins.  Some Applications of Mathematics to Breeding Problems III. , 1917, Genetics.

[2]  R. Punnett,et al.  The Genetical Theory of Natural Selection , 1930, Nature.

[3]  R. A. Fisher,et al.  The Genetical Theory of Natural Selection , 1931 .

[4]  H. Grüneberg,et al.  Introduction to quantitative genetics , 1960 .

[5]  M. Kimura,et al.  An introduction to population genetics theory , 1971 .

[6]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

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

[8]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[9]  T. Nagylaki.  Introduction to Theoretical Population Genetics , 1992 .

[10]  Wray L. Buntine,et al.  Learning classification trees , 1992 .

[11]  Heinz Mühlenbein,et al.  The Science of Breeding and Its Application to the Breeder Genetic Algorithm (BGA) , 1993, Evolutionary Computation.

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

[13]  Heinz Mühlenbein,et al.  Estimating the Heritability by Decomposing the Genetic Variance , 1994, PPSN.

[14]  Martin Pelikan,et al.  Hill Climbing with Learning (An Abstraction of Genetic Algorithm) , 1995 .

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

[16]  H. Mühlenbein,et al.  Gene Pool Recombination in Genetic Algorithms , 1996 .

[17]  Steven L. Salzberg,et al.  On growing better decision trees from data , 1996 .