By Iztok Fister, Iztok Fister Jr.
This conscientiously edited publication takes a stroll via contemporary advances in variation and hybridization within the Computational Intelligence (CI) area. It involves ten chapters which are divided into 3 components. the 1st half illustrates heritage info and gives a few theoretical beginning tackling the CI area, the second one half offers with the difference in CI algorithms, whereas the 3rd half makes a speciality of the hybridization in CI.
This booklet can function an incredible reference for researchers and scholars of machine technology, electric and civil engineering, economic climate, and average 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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Additional resources for Adaptation and Hybridization in Computational Intelligence
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HyperNEAT is able to capture symmetries in the geometric representation of the task and was extended by Risi into Evolvable Substrate HyperNEAT (ES-HyperNEAT) which added adaptive density of hidden neurons . Evolution of adaptive networks using improved developmental encoding that outperformed HyperNEAT was proposed by Suchorzewski . A multi-objective approach to the evolution of ART networks with adaptive parameters for the genetic algorithm was proposed in a PhD thesis by Kaylani .
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