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Maximizing Genetic Algorithm Efficiency with Linkage Learning

Discover how linkage learning enhances genetic algorithms by preserving functional dependencies between genes during crossover, leading to improved performance. Explore mechanisms such as messy GA and Bayesian optimization algorithm.

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Maximizing Genetic Algorithm Efficiency with Linkage Learning

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  1. The Use of Linkage Learning in Genetic Algorithms By David Newman

  2. Genetic Algorithms: Recap • Search Algorithm that uses Mechanisms of Natural Selection • Parameter Sets (Genomes) have Fitness Values • Higher Fitness Value = Higher Probability of Selection • Selected Genomes used to produce Next Generation • Directly Copied • Mutation • Crossover between Two Genomes • Mutation & Crossover

  3. Linkage Learning • Why Learn Linkage? • Reduces the Probability that sets of Functional Dependent Values are split up when Crossover is performed • What is Linkage Learning? • The Ability to Learn Functional Dependency between Genes • How is Linkage Learnt? • Improving Genetic Linkage • Distance between Functionally Dependent Genes • Store Functionally Dependent Relationships

  4. Linkage Learning GAs • Messy GA (mGA) • Incremental Commitment GA (ICGA) • BOA (Bayesian Optimization Algorithm) • Hierarchical BOA (hBOA) • Harik’s “Learning Linkage” GA

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