Linkage Learning in Evolutionary Algorithms
This overview explores the recent advancements in Linkage Learning within evolutionary algorithms, focusing on various recombination methods. It discusses classic approaches such as N-point and uniform crossover, highlighting their limitations in preserving beneficial gene configurations. Key concepts of linkage learning, including perturbation-based methods, linkage adaptation, and the probabilistic modeling of gene interactions, are examined. The document emphasizes the importance of maintaining gene linkages to enhance the efficiency of genetic algorithms, showcasing techniques like the Messy Genetic Algorithm and the Linkage Identification by Nonlinearity Check.
Linkage Learning in Evolutionary Algorithms
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Presentation Transcript
Recombination • Recombination explores the search space • Classic Recombination • N-point crossover • Uniform • Limitation • Disrupting good partial solutions via crossover is problematic Missouri University of Science and Technology
Linkage Learning • Linkage learning focuses on keeping linked genes together • Main classifications of linkage learning • Perturbation-based • Linkage Adaption • Probabilistic Model Building / Estimation of Distribution algorithms Missouri University of Science and Technology
Perturbation-based Methods • Metrics for determining linkage • Non-linear • Non-monotonic • Epitasis • Process • Two gene locations examined • Calculate fitness after perturbing each location separately and both together • Calculate metric • Add to a linkage set if metric indicates link Missouri University of Science and Technology
Perturbation-based Methods • Messy Genetic Algorithm • Linkages identified during evolution • Genes encoded as gene, allele pairs • Partial solutions are combined • Linkage identification and nonlinearity check procedure • Identification separated from the evolutionary process • Linkage information used to avoid linkage breaks in recombination Missouri University of Science and Technology
Messy Genetic Algorithm • Messy string: ((2 1), (1 0), (2 0)) • Underspecified (3-bit problem) • Use a template to determine unidentified bits • Template of (0,0,0) gives (0,1,0) • Overspecified (2-bit problem) • First appearance from left to right provides the value for a location • Cut-and-splice recombination • Cut: severs a string with pc probability • Probability corresponds to string length • Splice: joins two strings with ps probability Missouri University of Science and Technology
Messy Genetic Algorithm • 2 phase evolutionary process • Primordial • Deals with small string segments – Building Blocks • Building Blocks are reproduced to generate good quality pieces • Juxtapositional • Cut, splice and other genetic operators are involved to combine the good Building Blocks • Full solutions are formed Missouri University of Science and Technology
Linkage Identification by Nonlinearity Check (LINC) • Non-linearity ∆F1 + ∆F2 = ∆F12 ∆F1 = change in fitness from perturbing locus 1 ∆F2 = change in fitness from perturbing locus 2 ∆F12 = change in fitness from perturbing locus 1 & 2 Due to noise in fitness, linkage identified with |∆F12 – (∆F1 + ∆F2)| > ε Missouri University of Science and Technology
Linkage Identification by Nonlinearity Check (LINC) 1 1 0 0 1 F=5 0 1 0 0 1 F=6 ∆F1 = 1 1 0 0 0 1 1110 1 F=8 ∆F2 =3 F=4 ∆F2 = -1 0110 1 0 0 0 0 1 F=5 ∆F12 = 0 F=6 ∆F12 = 1 |∆F12 – (∆F1 + ∆F2)| > ε |0 – (1 + -1)| > 1 No Linkage |1– (1+3)| > 1Linkage Found Missouri University of Science and Technology
Linkage Adaption • Borrows from gene representation and modification in biology • Movable genes • Non-coding segments • Early techniques • Punctuation marks • Metabits • Linkage Evolving Genetic Operator Missouri University of Science and Technology
Punctuation Marks 1 ’ 1 0 0 1 0 ’ 1 1 1 0 0 1 ’ 1 1 0 ’ 1 0 1 ’ 0 0 Recombination 1 ’ 1 ’0 0 1 0 ’ 0 1 ’ 1 0 Missouri University of Science and Technology
Metabits 11 11 00 00 01 00 11 01 01 00 1001 0101 10 01 10 01 00 00 • Recombination • If both metabits are 1, crossover prob = .1 • Otherwise, crossover prob = .01 11 11 00 00 01 00 10 01 00 00 Missouri University of Science and Technology
Linkage Evolving Genetic Operator 1 1’ ‘0 0 1 ‘0’ ‘1 1’ ‘1’ ‘0 0‘1’ ‘1 1’ ‘0 1 0’ 1 0 0 • Recombination • Punctuation marks next to each other indicate linked genes • Crossover can’t occur between linkages 1 1’ ‘01’ ‘0 1 0’ 1’ ‘1’ ‘0 Missouri University of Science and Technology
Linkage Adaption • Linkage Learning Genetic Algorithm • Recent technique • Specialized chromosome representation • Movable genes • Non-coding segments • Probabilistic expression • Promoters • Linkage represented by the distance between genes Missouri University of Science and Technology
Probabilistic Expression Point of Interpretation A: (5,1) (4,0) (4,1) (3,0) (3,1)(5,0) = **001 B: (4,0) (4,1) (3,0) (3,1) (5,0)(5,1) = **000 Missouri University of Science and Technology
Probabilistic Model Building • Statistical models of the current generation generate new solutions • Early linkage learning • pairwise statistical measurements • Advanced linkage learning • Dependency trees • Bayesian networks • Marginal product models Missouri University of Science and Technology
Linkage Tree Genetic Algorithm • Statistical linkage learning process • Standard EA structure • Process • Linkage tree built every generation using hierarchal clustering • Linkage tree traversed to create crossover masks for offspring creation • Two parents compete with offspring pair • Two best continue down linkage tree Missouri University of Science and Technology
