Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Finite Markov chain results in evolutionary computation: a tour d'horizon
Fundamenta Informaticae
The theory of evolution strategies
The theory of evolution strategies
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Analyzing the (1, λ) evolution strategy via stochastic approximation methods
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Convergence results for the (1, λ)-SA-ES using the theory of ϕ-irreducible Markov chains
Theoretical Computer Science
Reconsidering the progress rate theory for evolution strategies in finite dimensions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
How the (1 + 1) ES using isotropic mutations minimizes positive definite quadratic forms
Theoretical Computer Science - Foundations of genetic algorithms
Algorithmic analysis of a basic evolutionary algorithm for continuous optimization
Theoretical Computer Science
Convergence Analysis of Evolution Strategies with Random Numbers of Offspring
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Mutative self-adaptation on the sharp and parabolic ridge
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Conditioning, halting criteria and choosing λ
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Log-linear convergence and optimal bounds for the (1 + 1)-ES
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Theoretical analysis of evolutionary computation on continuously differentiable functions
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Bandit-based estimation of distribution algorithms for noisy optimization: rigorous runtime analysis
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Finding pre-images via evolution strategies
Applied Soft Computing
Information Sciences: an International Journal
The convergence of a multi-objective evolutionary algorithm based on grids
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
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
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This paper presents simple proofs for the global convergence of evolution strategies in spherical problems. We investigate convergence properties for both adaptive and self-adaptive strategies. Regarding adaptive strategies, the convergence rates are computed explicitly and compared with the results obtained in the so-called "rate-of-progress" theory. Regarding self-adaptive strategies, the computation is conditional to the knowledge of a specific induced Markov chain. An explicit example of chaotic behavior illustrates the complexity in dealing with such chains. In addition to these proofs, this work outlines a number of difficulties in dealing with evolution strategies.