Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Are Evolutionary Algorithms Improved by Large Mutations?
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
An Experimental Investigation of Self-Adaptation in Evolutionary Programming
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Statistical Characteristics of Evolution Strategies
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
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Evolution Strategies(ES) are an approach to numerical optimization that shows good optimization performance. However, according to our computer simulations, ES shows different optimization performance when a different lower bound of strategy parameters is adopted. We analyze that this is caused by the premature convergence of strategy parameters, although they are traditionally treated as "self-adaptive" parameters. This paper proposes a new extended ES, called RES in order to overcome this brittle property. RES has redundant neutral strategy parameters and adopts new mutation mechanisms in order to utilize the effect of genetic drift to improve the adaptability of strategy parameters. Computer simulations of the proposed approach are conducted using several test functions.