Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Neutrality and self-adaptation
Natural Computing: an international journal
On the Optimization of Monotone Polynomials by Simple Randomized Search Heuristics
Combinatorics, Probability and Computing
Finding needles in haystacks is harder with neutrality
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Rigorous hitting times for binary mutations
Evolutionary Computation
Population sizing for the redundant trivial voting mapping
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Comparing variants of MMAS ACO algorithms on pseudo-boolean functions
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
IEEE Transactions on Evolutionary Computation
Theoretical analysis of rank-based mutation: combining exploration and exploitation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Representation techniques are important issues when designing successful evolutionary algorithms. Within this field the use of neutrality plays an important role. We examine the use of bit-wise neutrality introduced by Poli and López (2007) from a theoretical point of view and show that this mechanism only enhances mutation-based evolutionary algorithms if not the same number of genotypic bits for each phenotypic bit is used. Using different numbers of genotypic bits for the bits in the phenome we point out by rigorous runtime analyses that it may reduce the optimization time significantly.