Global optimization
Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Biologically influenced algorithms and parallelism in non-linear optimization
Biologically influenced algorithms and parallelism in non-linear optimization
Adaptive global optimization with local search
Adaptive global optimization with local search
New ideas in optimization
Direct search methods: then and now
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
The theory of evolution strategies
The theory of evolution strategies
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
Proceedings of the 5th International Conference on Genetic Algorithms
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
On The Convergence Properties Of A Simple Self-adaptive Evolutionary Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
On the Application of Evolutionary Pattern Search Algorithms
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms
Evolutionary Computation
Convergence in Evolutionary Programs with Self-Adaptation
Evolutionary Computation
A Convergence Analysis of Unconstrained and Bound Constrained Evolutionary Pattern Search
Evolutionary Computation
Analyzing the (1, λ) evolution strategy via stochastic approximation methods
Evolutionary Computation
Evolutionary pattern search algorithms for unconstrained andlinearly constrained optimization
IEEE Transactions on Evolutionary Computation
A Filter-Based Evolutionary Algorithm for Constrained Optimization
Evolutionary Computation
A particle swarm pattern search method for bound constrained global optimization
Journal of Global Optimization
The impact of parametrization in memetic evolutionary algorithms
Theoretical Computer Science
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Recent convergence analyses of evolutionary pattern search algorithms (EPSAs) have shown that these methods have a weak stationary point convergence theory for a broad class of unconstrained and linearly constrained problems. This paper describes how the convergence theory for EPSAs can be adapted to allow each individual in a population to have its own mutation step length (similar to the design of evolutionary programing and evolution strategies algorithms). These are called locally-adaptive EPSAs (LA-EPSAs) since each individual's mutation step length is independently adapted in different local neighborhoods. The paper also describes a variety of standard formulations of evolutionary algorithms that can be used for LA-EPSAs. Further, it is shown how this convergence theory can be applied to memetic EPSAs, which use local search to refine points within each iteration.