By Jens Gottlieb, Günther R. Raidl

This publication constitutes the refereed lawsuits of the sixth ecu convention on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2006, held in Budapest, Hungary in April 2006.The 24 revised complete papers provided have been rigorously reviewed and chosen from seventy seven submissions. The papers disguise evolutionary algorithms in addition to numerous different metaheuristics, like scatter seek, tabu seek, memetic algorithms, variable local seek, grasping randomized adaptive seek systems, ant colony optimization, and particle swarm optimization algorithms. The papers care for representations, heuristics, research of challenge buildings, and comparisons of algorithms. The record of studied combinatorial optimization difficulties contains popular examples like graph coloring, knapsack difficulties, the touring salesclerk challenge, scheduling, graph matching, in addition to particular real-world difficulties.

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Additional resources for Evolutionary Computation in Combinatorial Optimization: 6th European Conference, Evocop 2006, Budapest, Hungary, April 10-12, 2006, Proceedings

Example text

Finally the results are compared. We evolve the structure of a PSO algorithm and then we asses its performance by comparing it with the standard PSO algorithm. 1 Test Functions Eleven test problems are used in order to asses the performance of the evolved EA. Functions f1 − f6 are unimodal test function. Functions f7 − f10 are highly multi modal (the number of the local minimum increases exponentially with problem’s dimension [17]). Functions f1 − f 10 are given in Table 1. Function f11 corresponds to the constrained portfolio optimization problem.

The neighbor solution is then generated by increasing pipe diameters. A random pipe (whose diameter is not the largest available one) is selected and its diameter is increased to the next available size. This move always keeps the network valid regarding the pressure constraints, but may cause the selected pipe to violate the upstream requirement. This is easily fixed by recursively increasing the diameter of the upstream pipe of the altered pipes while necessary. 4 Tabu Moves and Aspiration Criterion Either if the pipe diameter is decreased or increased, the branch which diameter was changed receives a “tabu” status.

R. Storn, K. Price, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces”, Global Optimization, Vol. 11, pp. 341-359, 1997. 36 L. Dio¸san and M. Oltean 15. J. ), “On the evolution of evolutionary algorithms”, in Keijzer, M. ) editors, European Conference on Genetic Programming, pp. 389-398, Springer-Verlag, Berlin, 2004. 16. H. Xiaohui, S. Yuhui, R. Eberhart, “Recent Advances in Particle Swarm”, Proceedings of the IEEE Congress on Evolutionary Computation, pp.

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