An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary computation: toward a new philosophy of machine intelligence
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Two self-adaptive crossover operators for genetic programming
Advances in genetic programming
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Proceedings of the 16th annual international conference of the Center for Nonlinear Studies on Landscape paradigms in physics and biology : concepts, structures and dynamics: concepts, structures and dynamics
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Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
An overview of parameter control methods by self-adaption in evolutionary algorithms
Fundamenta Informaticae
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Concrete Mathematics: A Foundation for Computer Science
Concrete Mathematics: A Foundation for Computer Science
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Information Processing Letters
Neutrality: a necessity for self-adaptation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
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IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Effects of phenotypic redundancy in structure optimization
IEEE Transactions on Evolutionary Computation
When to use bit-wise neutrality
Natural Computing: an international journal
Some steps towards understanding how neutrality affects evolutionary search
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multi-objective model selection for support vector machines
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm
Computational Optimization and Applications
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Neutral genotype-phenotype mappings can be observed in natural evolution and are often used in evolutionary computation. In this article, important aspects of such encodings are analyzed.First, it is shown that in the absence of external control neutrality allows a variation of the search distribution independent of phenotypic changes. In particular, neutrality is necessary for self-adaptation, which is used in a variety of algorithms from all main paradigms of evolutionary computation to increase efficiency.Second, the average number of fitness evaluations needed to find a desirable (e.g., optimally adapted) genotype depending on the number of desirable genotypes and the cardinality of the genotype space is derived. It turns out that this number increases only marginally when neutrality is added to an encoding presuming that the fraction of desirable genotypes stays constant and that the number of these genotypes is not too small.