On the influence of the representation granularity in heuristic forma recombination
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Utilizing Dynastically Optimal Forma Recombination in Hybrid Genetic Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Genetic forma recombination in permutation flowshop problems
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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A family of recombination operators is studied in this work. These operators are based on keeping and using certain information about the past evolution of the algorithm to guide the recombination process. Within this framework, several recombination operators are specifically designed to preserve diversity within the population, while avoiding implicit mutations. The empirical evaluation of these operators on instances of two test problems (k--EMP and permutation flowshop) shows an improvement of the results with respect to other classical operators. This improvement seems to related to the increasing degree of epistasis of the problem.