Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 5th International Conference on Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Learning building block structure from crossover failure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Finding building blocks through eigenstructure adaptation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Tightness time for the linkage learning genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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For binary-represented problems with strong building block structure and low epistasis, one-point crossover success (both offspring are better or one is better and the other not worse) and crossover failure (both offspring are worse or one worse and one not better) indicate that a building block was produced or broken near the crossover point. Starting from such a crossover point, and flipping bits ahead of and behind the crossover point until the fitness no longer decreases, gives an indication of the building block extent. The resulting string can be extracted and used to replace the corresponding part of the best individual found to date, to increase its fitness. Experiments on test functions Royal Road R1 and R2, Holland's Royal Road Challenge function and the H-IFF function show that such a method could improve performance significantly on the first 3 functions but be trapped on the last, relative to a classical GA.