Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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Presence of uncertainty in the search environment of Evolutionary algorithms (EA) interferes with the evaluation and the selection process of EA and adversely affects the performance of the algorithm. Presence of noise also means fitness function can not be evaluated and it has to be estimated instead. Of the various approaches which been tried to handle uncertainty in EA search environment, the more familiar approaches are: introduction of diversity (hyper mutation, random immigrants, special operators); and incorporation of memory of the past (diploidy, case based memory) [6]. In [2], we proposed a method, DPGA (distributed population evolutionary algorithm) that uses a distributed population architecture to simulate a distributed, self-adaptive memory of the solution space. Local regression is used in each sub-population to estimate the fitness. In the current research, we further investigate performance of DPGA for noisy fitness function i.e. fitness of any solution is altered by the addition of a noise term . .Noisy' versions of few standard benchmark problems have been considered in the simulation runs of the DPGA algorithm.