Titlebook: Adaptation and Hybridization in Computational Intelligence; Iztok Fister,Iztok Fister Jr. Book 2015 Springer International Publishing Swit

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书目名称Adaptation and Hybridization in Computational Intelligence影响因子(影响力)




书目名称Adaptation and Hybridization in Computational Intelligence影响因子(影响力)学科排名




书目名称Adaptation and Hybridization in Computational Intelligence网络公开度




书目名称Adaptation and Hybridization in Computational Intelligence网络公开度学科排名




书目名称Adaptation and Hybridization in Computational Intelligence被引频次




书目名称Adaptation and Hybridization in Computational Intelligence被引频次学科排名




书目名称Adaptation and Hybridization in Computational Intelligence年度引用




书目名称Adaptation and Hybridization in Computational Intelligence年度引用学科排名




书目名称Adaptation and Hybridization in Computational Intelligence读者反馈




书目名称Adaptation and Hybridization in Computational Intelligence读者反馈学科排名

一大群

https://doi.org/10.1007/978-1-4020-8227-6e used in order to make a DE solver more robust, efficient, etc., and to overcome parameter tuning which is usually a time-consuming task needed to be done before the actual optimization process starts. Literature overviews of adaptive and self-adaptive mechanisms are mainly focused on mutation and

用手捏

AFPM Machines Without Stator Cores,gy parameters (i.e. mutation strengths) that are embedded into representation of individuals. The mutation strengths determine the direction and the magnitude of the changes on the basis of the new position of the individuals in the search space is determined. This chapter analyzes the characteristi

尖牙

https://doi.org/10.1007/978-1-4020-8227-6al search directions are grouped in a suitable number of subcomponents. Then, different subpopulations are assigned to the subcomponents and evolved using an optimization metaheuristic. To evaluate the fitness of individuals, the subpopulations cooperate by exchanging information. In this chapter we

悦耳

古代

NICE

AFPM Machines Without Stator Cores,. In order to avoid this hard work, the automatic tuning of these parameters is proposed. A real-coded genetic algorithm (GA) was developed for this purpose. This, so-called meta-GA, algorithm acts as a meta-heuristic that searches for the optimal values of ANN parameters using the genetic operators

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Ibd810

https://doi.org/10.1007/978-1-4020-8227-6ing problems with stochastic variables. More precisely, we solve one problem with stochastic customers, the Probabilistic Traveling Salesman Problem and one problem with stochastic demands, the Vehicle Routing Problem with Stochastic Demands. The proposed algorithm uses a Variable Neighborhood Searc

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