Projected gradient methods for linearly constrained problems
Mathematical Programming: Series A and B
Global convergence of a class of trust region algorithms for optimization with simple bounds
SIAM Journal on Numerical Analysis
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Pattern Search Algorithms for Bound Constrained Minimization
SIAM Journal on Optimization
Pattern Search Methods for Linearly Constrained Minimization
SIAM Journal on Optimization
On the Application of Evolutionary Pattern Search Algorithms
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Computation
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Analyzing the (1, λ) evolution strategy via stochastic approximation methods
Evolutionary Computation
Local convergence rates of simple evolutionary algorithms withCauchy mutations
IEEE Transactions on Evolutionary Computation
Genetic Programming and Autoconstructive Evolution with the Push Programming Language
Genetic Programming and Evolvable Machines
Locally-adaptive and memetic evolutionary pattern search algorithms
Evolutionary Computation
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Local and global order 3/2 convergence of a surrogate evolutionary algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
A theoretical model and convergence analysis of memetic evolutionary algorithms
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
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We present and analyze a class of evolutionary algorithms for unconstrained and bound constrained optimization on Rn: evolutionary pattern search algorithms (EPSAs). EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. We show that EPSAs can be cast as stochastic pattern search methods, and we use this observation to prove that EPSAs have a probabilistic, weak stationary point convergence theory. This convergence theory is distinguished by the fact that the analysis does not approximate the stochastic process of EPSAs, and hence it exactly characterizes their convergence properties.