Theoretical and Empirical Investigations on Difficulty in Structure Learning by Estimation of Distribution Algorithms

Estimation of distribution algorithms (EDAs) are population based evolutionary algorithms derived from genetic algorithms (GAs) . EDAs build probabilistic models of promising solutions to guide further exploration of the search space. They have been considered to behave in similar way to GAs. In this paper, we show their different behaviors and difficulties in applications of EDAs by designing an EDA difficult function in which schemata that are not consistent with problem structure sometimes overwhelm those that are.

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

[2]  Stefan Droste,et al.  Not all linear functions are equally difficult for the compact genetic algorithm , 2005, GECCO '05.

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

[4]  Dirk Thierens,et al.  Linkage Information Processing In Distribution Estimation Algorithms , 1999, GECCO.

[5]  Heinz Mühlenbein,et al.  FDA -A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions , 1999, Evolutionary Computation.

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

[7]  P. Bosman,et al.  Continuous iterated density estimation evolutionary algorithms within the IDEA framework , 2000 .

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

[9]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

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

[11]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[12]  Heinz Mühlenbein,et al.  The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

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

[14]  David E. Goldberg,et al.  The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..