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The Use of Linkage Learning in Genetic Algorithms

The Use of Linkage Learning in Genetic Algorithms. By David Newman. Genetic Algorithms: Recap. Search Algorithm that uses Mechanisms of Natural Selection Parameter Sets (Genomes) have Fitness Values Higher Fitness Value = Higher Probability of Selection

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The Use of Linkage Learning in Genetic Algorithms

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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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