Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Biases in the crossover landscape
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive Non-uniform Crossover Based On Statistics For Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
An Adaptive Genetic Algorithm for Solving Traveling Salesman Problem
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off
Applied Soft Computing
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Genetic Algorithms (GAs) emulate the natural evolution process and maintain a population of potential solutions to a given problem. Through the population, GAs implicitly maintain the statistics about the search space. This implicit statistics can be used explicitly to enhance GA's performance. Inspired by this idea, a statistics-based adaptive non-uniform crossover (SANUX) has been proposed. SANUX uses the statistics information of the alleles in each locus to adaptively calculate the swapping probability of that locus for crossover operation. A simple triangular function has been used to calculate the swapping probability. In this paper two new functions, the trapezoid and exponential functions, are proposed for SANUX instead of the triangular function. Experiment results show that both functions further improve the performance of SANUX.