Theory of evolution strategies - a tutorial
Theoretical aspects of evolutionary computing
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Qualms regarding the optimality of cumulative path length control in CSA/CMA-evolution strategies
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Weighted multirecombination evolution strategies
Theoretical Computer Science - Foundations of genetic algorithms
How the (1 + 1) ES using isotropic mutations minimizes positive definite quadratic forms
Theoretical Computer Science - Foundations of genetic algorithms
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
On the use of evolution strategies for optimising certain positive definite quadratic forms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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This paper proposes the σ-self-adaptive weighted multirecombination evolution strategy (ES) and presents a performance analysis of this newly engineered ES. The steady state behavior of this strategy is investigated on the sphere model and a formula for the optimal choice of the learning parameter is derived allowing the ES to reach maximal performance. A comparison between weighted multirecombination ES with σ-self-adaptation (σSA) and with cumulative step size adaptation (CSA) shows that the σ-self-adaptive ES can exhibit the same performance and can even outperform its CSA counterpart for a range of learning parameters.