Download Adaptation and Hybridization in Computational Intelligence by Iztok Fister, Iztok Fister Jr. PDF

By Iztok Fister, Iztok Fister Jr.

This conscientiously edited publication takes a stroll via fresh advances in version and hybridization within the Computational Intelligence (CI) area. It contains ten chapters which are divided into 3 components. the 1st half illustrates heritage info and gives a few theoretical starting place tackling the CI area, the second one half bargains with the variation in CI algorithms, whereas the 3rd half specializes in the hybridization in CI.

This ebook can function an amazing reference for researchers and scholars of computing device technological know-how, electric and civil engineering, economic system, and typical sciences which are faced with fixing the optimization, modeling and simulation difficulties. It covers the new advances in CI that surround Nature-inspired algorithms, like synthetic Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.

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Extra resources for Adaptation and Hybridization in Computational Intelligence

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36]. The former regulates the probability that the crossover operator will be applied to two or more parents, while the latter the probability that the mutation will change a generated offspring. The parameters CR and F are used in DE for the same purposes. The other SI-based and ANN algorithms use specific algorithm parameters depending on the biological, physical, chemical, and all other rules that inspire developers of the new algorithms [30]. An instance of parameter values set during the run is also-called a parameter setting.

Hybridization of Components. The EA domain has been matured over more than 50 years of development. Small numbers of problems in science, as well as in practice, remain intact by the evolutionary approach. In line with this, many prominent experts have emerged within this domain together with several original solutions developed by solving this huge diapason of problems. These original solutions were mostly tackled for developing new evolutionary operators, population models, elitism, etc. Typically, SI-based algorithms borrow the DE operators of mutation and crossover that replace the original move operator in order to increase the efficiency of the SI-based search process.

Their algorithms combined a highly specialized crossover operators with Tabu search [83]. GA-EDA [104] is a good example of a hybrid EA which uses genetic and estimation of distribution algorithms. Niknam [102] developed a new EA algorithm called DPSO-HBMO, which based on the combination of honey bee mating optimization [87] and discrete PSO [171]. Lin [98] proposed a new EA combining DE with the real-valued GA. 6 Hybrid SI-Based Algorithms In order to improve original SI-based algorithms, researchers usually hybridized these with other meta-heuristics, different local searches, fuzzy logic, machine learning methods and other mathematical principles.

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