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

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Stephanie Mckendry,Matson Lawrenceosed transformations. This method of the proof was chosen instead of sufficient decrease approach since the crucial element of the presented proof is an extension of the SQI convergence proof from [14] which is based on this approach.

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Peter A. Wilderer,Michael von Hauffrom precedence constraints, and (iii) project uncertainties. We also present a hybrid meta heuristic (HMH) combining a genetic algorithm with simulated annealing to solve discrete version of multiobjective TCT problem. HMH is employed to solve two test cases of TCT.

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1867-4534 real-world insights gained by experience in computational in.This volume presents a collection of recent studies covering the spectrum of computational intelligence applications with emphasis on their application to challenging real-world problems. Topics covered include: Intelligent agent-based alg

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A Novel Optimization Algorithm Based on Reinforcement Learning,t learning principle to determine the particle move in search for the optimum process. A model of successful actions is build and future actions are based on past experience. The step increment combines exploitation of the known search path and exploration for the improved search direction. The algo

ingrate