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
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (Vienna Series in Theoretical Biology)
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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Any crossover operator has both beneficial and detrimental effects: it can bring building blocks together or it can tear them apart. In this paper, we provide evidence that the recombination can be biased towards its more beneficial aspects by modifying both the parent selection process and the number of children created by each pair of parents. We exclude both high rank and low rank individuals from being selected as parents. The new idea is that the worst individuals do not have valuable building blocks to contribute, and it is too risky to subject the best individuals to crossover and have their building blocks separated. In a further refinement, we allow the number of children per family to be correlated to the diversity of their parents, and thus increase the pressure of sibling rivalry (competition). These ideas are tested on well-known test functions such as the hierarchical if-and-only-if, royal road, concatenated trap functions and the one dimensional Ising spin model. Four different parent selection schemes are compared and simulations are shown for both two children (fixed) and many children (variable) families. The results indicate that these changes are beneficial for a wide class of problems.