Fitness distributions: tools for designing efficient evolutionary computations
Advances in genetic programming
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
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Operator adaptation in evolutionary programming is investigated from both population level and individual level in this paper. The updating rule for operator adaptation is defined based on the fitness distributions at population level compared to the immediate reward or punishment from the feedback of mutations at individual level. Through observing the behaviors of operator adaptation in evolutionary programming, it is discovered that a small-stepping operator could become a dominant operator when other operators have rather larger step sizes. Therefore, it is possible that operator adaptation could lead to slow evolution when operators are adapted freely by themselves.