Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Locally-adaptive and memetic evolutionary pattern search algorithms
Evolutionary Computation
Mathematics and Computers in Simulation
How the (1 + 1) ES using isotropic mutations minimizes positive definite quadratic forms
Theoretical Computer Science - Foundations of genetic algorithms
Using genetic algorithms in software optimization
TELE-INFO'07 Proceedings of the 6th WSEAS Int. Conference on Telecommunications and Informatics
Analysis of a simple evolutionary algorithm for minimization in euclidean spaces
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Selecting small audio feature sets in music classification by means of asymmetric mutation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Rigorous runtime analysis of the (1+1) ES: 1/5-rule and ellipsoidal fitness landscapes
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Parameter-less algorithm for evolutionary-based optimization
Computational Optimization and Applications
An artificial fish swarm algorithm based hyperbolic augmented Lagrangian method
Journal of Computational and Applied Mathematics
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Evolutionary programs are capable of finding good solutions to difficult optimization problems. Previous analysis of their convergence properties has normally assumed the strategy parameters are kept constant, although in practice these parameters are dynamically altered. In this paper, we propose a modified version of the 1/5-success rule for self-adaptation in evolution strategies (ES). Formal proofs of the long-term behavior produced by our self-adaptation method are included. Both elitist and non-elitist ES variants are analyzed. Preliminary tests indicate an ES with our modified self-adaptation method compares favorably to both a non-adapted ES and a 1/5-success rule adapted ES.