An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
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
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Controlling Crossover through Inductive Learning
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
IEEE Transactions on Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Exploring Building Blocks through Crossover
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
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In the classical binary genetic algorithm, although crossover within a building block (BB) does not always cause a decrease in fitness, any decrease in fitness results from the destruction of some building blocks, in problems where such structures are well defined, such as those considered here. Those crossovers that cause both offspring to be worse, or one to be worse and one unchanged, are here designated as failed crossovers. Counting the failure frequency of single-point crossovers performed at each locus reveals something of the BB structure. Guided by the failure record, GA operators could choose appropriate points for crossover, in order to work moreefficiently and effectively. Experiments on test functions RoyalRoad R1 and R2, Holland's Royal Road Challenge function and H-IFF functions show that such a guided operator improves performance. While many methods exist to discover building blocks, this "quick-and-dirty" method can sketch the linkage nearly "for free", requiring very little extra computation.