Titlebook: Computational Intelligence in Optimization; Applications and Imp Yoel Tenne,Chi-Keong Goh Book 2010 Springer-Verlag Berlin Heidelberg 2010

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Multi-Objective Optimization Using Surrogates,l or experimental expense involved. Practical multiobjective optimization was considered almost as a utopia even in academic studies due to the multiplication of this expense. This paper discusses the idea of using surrogate models for multiobjective optimization. With recent advances in grid and pa

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A Review of Agent-Based Co-Evolutionary Algorithms for Multi-Objective Optimization, algorithms allow for existing many species and sexes of agents within the system as well as for defining co-evolutionary interactions between species and sexes. Algorithms based on the model of co-evolutionary multi-agent system have been already applied in many domains, like multi-modal optimizati

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A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems,lti-agent framework in which a number of existing algorithms, cast as agents, are deployed with the aim to solve the problem in hand as efficiently as possible. The key factor for the success of this approach is a dynamic resource allocation biased toward promising algorithms on the given problem. T

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An Integrated Approach to Speed Up GA-SVM Feature Selection Model,-SVM are desirable in filtering out the irrelevant features and thus improve the accuracy; the selection itself might also offer critical insights into the problems. However, the high computational cost greatly discourages the application of GA-SVM, especially for large-scale datasets. In this paper

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Computation in Complex Environments;,he word; complexity addresses, amongst others, non-linear, contingent and ‘chaotic’ phenomena ([10],[11]). Many thinkers on complexity consider such characteristics - sometimes called .-to demarcate a transition point where analytical approaches are no longer feasible ([26]:18).

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