Theoretical Analysis of Simplex Crossover for Real-Coded Genetic Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Editorial Real coded genetic algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A robust real-coded evolutionary algorithm with toroidal search space conversion
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
On self-adaptive features in real-parameter evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Parameters of real-valued crossover operators have been often tuned under a constraint for preserving statistics of infinite parental population. For applications in actual scenes, in a previous study, an alternative constraint, called unbiased constraint , considering finiteness of the population has been derived. To clarify the wide applicability of the unbiased constraint, this paper presents two additional studies: (1) applying it to various crossover operators in higher dimensional search space, and (2) generalization of it for preserving statistics of overall population. Appropriateness of the parameter setting based on the unbiased constraint has been supported in discussion on robust search.