The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
Demonstrating the evolution of complex genetic representations: an evolution of artificial plants
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Factorial representations to generate arbitrary search distributions
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Compact representations as a search strategy: compression EDAs
Theoretical Computer Science - Foundations of genetic algorithms
An information geometry perspective on estimation of distribution algorithms: boundary analysis
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Estimation of distribution algorithms with mutation
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
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Correlations between alleles after selection are an important source of information. Such correlations should be exploited for further search and thereby constitute the building blocks of evolutionary exploration. With this background we analyze the structure of the offspring probability distribution, or exploration distribution, for a simple GA with mutation only and a crossover GA and compare them to Estimation-Of-Distribution Algorithms (EDAs). This will allow a precise characterization of the structure of exploration w.r.t. correlations in the search distribution for these algorithms. We find that crossover transforms, depending on the crossover mask, mutual information between loci into entropy. In total, it can only decrease such mutual information. In contrast, the objective of EDAs is to estimate the correlations between loci and exploit this information during exploration. This may lead to an effective increase of mutual information in the exploration distribution, what we define correlated exploration.