Locally-adaptive and memetic evolutionary pattern search algorithms
Evolutionary Computation
Real-coded memetic algorithms with crossover hill-climbing
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A hybrid genetic algorithm with pattern search for finding heavy atoms in protein crystals
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Filter-Based Evolutionary Algorithm for Constrained Optimization
Evolutionary Computation
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 describe a convergence theory for evolutionary pattern search algorithms (EPSA) on a broad class of unconstrained and linearly constrained problems. EPSA adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSA is inspired by recent analyzes of pattern search methods. Our analysis significantly extends the previous convergence theory for EPSA. Our analysis applies to a broader class of EPSA and it applies to problems that are nonsmooth, have unbounded objective functions, and are linearly constrained. Further, we describe a modest change to the algorithmic framework of EPSA for which a nonprobabilistic convergence theory applies. These analyses are also noteworthy because they are considerably simpler than previous analyses of EPSA