Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Search space boundary extension method in real-coded genetic algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Genetic algorithms with multi-parent recombination
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Multi-Parent's Niche: n-ary Crossovers on NK-Landscapes
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An analysis on crossovers for real number chromosomes in an infinite population size
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Genetic search of block-based structures of dynamical process models
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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In this paper we study some theoretical aspects of a new crossover operator for real-coded genetic algorithms based on the statistical features of the best individuals of the population. This crossover is based on defining a confidence interval for a localization estimator using the L2 norm. From this confidence interval we obtain three parents: the localization estimator and the lower and upper limits of the confidence interval. In this paper we analyze the mean and variance of the population when this crossover is applied, studying the behavior of the distribution of the fitness of the individuals in a problem of optimization. We also make a comparison of our crossover with the crossovers BLX-驴 and UNDX-m, showing the robustness of our operator.