Titlebook: Genetic and Evolutionary Computation — GECCO 2004; Genetic and Evolutio Kalyanmoy Deb Conference proceedings 2004 Springer-Verlag Berlin He

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Fitness Inheritance in the Bayesian Optimization Algorithmoncept in BOA, because population-sizing requirements for building appropriate models of promising solutions lead to good fitness estimates even if only a small proportion of candidate solutions is evaluated using the actual fitness function. This can lead to a reduction of the number of actual fitness evaluations by a factor of 30 or more.

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Let’s Get Ready to Rumble: Crossover Versus Mutation Head to Headthe mutation on additively separable problems perturbed with additive Gaussian noise. The results show that the speed-up of using BB-wise mutation on deterministic problems is ., where . is the BB size, and . is the number of BBs. Likewise, the speed-up of using crossover on stochastic problems with fixed noise variance is ..

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Clustering with Niching Genetic K-means Algorithmvarying size and varying number of local optima. The significance of NGKA is also shown on the experimental data sets by comparing through simulations with Genetically Guided Algorithm (GGA) and Genetic .-means Algorithm (GKA).

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John Morrissey,C. Patrick Heidkamparchical problems in quadratic or subquadratic number of function evaluations without the need for setting any parameters whatsoever. A number of experiments are presented to verify scalability of the parameter-less hBOA.

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Soil Formation, Quality, Sustainability,earch, the parameters of these distributions confirm good scalability of hBOA with local search. We further argue that standard performance measures for optimization algorithms—such as the average number of evaluations until convergence—can be misleading. Finally, our results indicate that for highl

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