Evolving artificial intelligence
Evolving artificial intelligence
A Markov Chain Analysis on A Genetic Algorithm
Proceedings of the 5th International Conference on Genetic Algorithms
Selected papers from the EEE/Nagoya-University World Wisepersons Workshop on Fuzzy Logic, Neural Networks, and Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
A note on the empirical evaluation of intermediate recombination
Evolutionary Computation
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
Information Sciences: an International Journal
Memetic algorithms for continuous optimisation based on local search chains
Evolutionary Computation
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
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This paper concerns recombinations which produce offspring from two parents. We assume an infinite population and regard recombinations as transformations of stochastic variables represented as chromosomes. We then formalize recombinations with the probability density functions of stochastic variables represented as the parameters and describe the change of the probability density functions of chromosomes before and after recombination. Our formalization includes various proposed recombinations, such as multi-point, uniform, and linear crossover, as well as BLX-α. We also derive certain properties of the operators, such as diversification and decorrelation.