S Marsili Libelli,P Alba.Adaptive mutation in genetic algorithms [J].Int J Adv Manuf Technol,2000,16:491-497.Marsili Libelli, S., Alba, P.: Adaptive mutations in genetic algorithms. Soft Comput. (2000)Marsili, S
the application of genetic algorithms in the multimodal function optimisation. In section 4, we outline the Dynamic Parametric AGA approach; of using adaptively varying probabilities of crossover and mutation for economic dispatch optimisation. In Section 5, we present the measures to quantify ...
Although there is limited mathematical evidence that evolutionary algorithms converge to global optima, they do often reach extremely good results [28,29]. 1.2.1 Genetic algorithm The GA is inspired by Charles Darwin's theory of natural evolution, by focusing on the selection, mutation, and cross...
Genetic algorithms33,34,35are stochastic search optimizers that are based on the concepts of evolution and natural selection. GAs are inherently parallel algorithms, which makes them able to take advantage of today’s parallel supercomputers to expedite the optimization task by a factor close to the...
Its global convergence speed and optimal solutions were all better than that of many other algorithms.doi:10.3923/itj.2010.974.978Yan TaishanAsian Network for Scientific InformationInformation Technology JournalAn Improved Adaptive Genetic Algorithm Based on Human Reproduction Mode for Solving the Knapsack...
In this Review, we first discuss the various mechanisms whereby HGT occurs, how the genetic signatures shape patterns of genomic variation and the distinct bioinformatic algorithms developed to detect these patterns. We then discuss the evolutionary theory behind HGT and positive selection in bacteria,...
genetic algorithmsneural netssearch problemssimulated annealingThis paper presents a new genetic algorithm (GA) with good convergence properties and a remarkable low computational load. Such features are achieved by on-line tuning up the probabilities of mutation and crossover on the basis of the ...
In general, six parameters must be set to conduct a GA search: (1) the population size (N), (2) the selection mode, genetic operators through (3) crossover and (4) mutation types, as well as their respective activation probabilities, (5) p\(_{c}\)and (6) p\(_{m}\). This ...
The genetic algorithm was performed using the following parameters: population size: 10, selection size: 3, mutation rate: 0.05, crossover rate: 0.01, minimum/maximum generations: 3/10, and binary tournament selection. To evaluate the performance of binning, we used the minimum information about ...
Topology optimization of a truss structure under dynamic loads is studied based onadaptive genetic algorithms. 采用自适应遗传算法求解了以脉冲激励下的动力响应作为约束条件、以结构重量最小化为目标函数的桁架结构拓扑优化问题。 3. To the non-linear mathematical model, a kind ofadaptive genetic algorithmsis...