The theory of evolution strategies
The theory of evolution strategies
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
On self-adaptive features in real-parameter evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Step length adaptation on ridge functions
Evolutionary Computation
Premature Convergence in Constrained Continuous Search Spaces
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Fitness Expectation Maximization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A novel approach to adaptive isolation in evolution strategies
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Mirrored sampling and sequential selection for evolution strategies
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Analyzing the impact of mirrored sampling and sequential selection in elitist evolution strategies
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
Cumulative step-size adaptation on linear functions
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
On the behaviour of the (1,λ)-σSA-ES for a constrained linear problem
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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This paper investigates σ-self-adaptation for real valued evolutionary algorithms on linear fitness functions. We identify the step-size logarithm log σ as a key quantity to understand strategy behavior. Knowing the bias of mutation, recombination, and selection on log σ is sufficient to explain σ-dynamics and strategy behavior in many cases, even from previously reported results on non-linear and/or noisy fitness functions. On a linear fitness function, if intermediate multi-recombination is applied on the object parameters, the i-th best and the i-th worst individual have the same σ-distribution. Consequently, the correlation between fitness and step-size σ is zero. Assuming additionally that σ-changes due to mutation and recombination are unbiased, then σ-self-adaptation enlarges σ if and only if µ