By Jens Gottlieb, Günther R. Raidl

This e-book 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 conscientiously reviewed and chosen from seventy seven submissions. The papers hide evolutionary algorithms in addition to numerous different metaheuristics, like scatter seek, tabu seek, memetic algorithms, variable local seek, grasping randomized adaptive seek tactics, ant colony optimization, and particle swarm optimization algorithms. The papers care for representations, heuristics, research of challenge constructions, and comparisons of algorithms. The checklist of studied combinatorial optimization difficulties comprises well-liked examples like graph coloring, knapsack difficulties, the touring salesclerk challenge, scheduling, graph matching, in addition to particular real-world difficulties.

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The order of update is 0, 1, ... SwarmSize−1). In 9 cases the evolved PSO performed better (on average) than the other algorithm. 05 level of significance. Before applying the T-test, an F-test has been used for determining whether the compared data have the same variance. The P-values of a two-tailed T-test with 499 degrees of freedom are given in Table 4. 05) in 9 cases (out of 11). Table 4. 12E-03 Evolving the Structure of the Particle Swarm Optimization Algorithms 4 35 Conclusion and Further Work A new hybrid technique for evolving the structure of a PSO algorithm has been proposed in this paper.

C2 = (6, 2, 1, 4, 7, 1, 6, 2) In this case particles 1, 2 and 6 are updated 2 times each and particles 0, 3, 5 are not updated at all. Because of that it is necessari to remove the useless 28 L. Dio¸san and M. Oltean particles and to scale the genes of the GA chromosome to the interval [0 ... 4]. The obtained chromosome is: C2 = (3, 1, 0, 2, 4, 0, 3, 1). The quality for this chromosome will be computed using a swarm of size 5 (5 swarm particles), performing the following 8 updates: update(Swarm[3]), update(Swarm[1]), update(Swarm[0]), update(Swarm[2]), update(Swarm[4]), update(Swarm[0]), update(Swarm[3]), update(Swarm[1]).

Their goal was to minimize the sum of investment and operational costs. On that paper, the considered networks were those which contain the fewest number of pipelines that can deliver gas from the fields to the separation plants or, in other words, networks with fixed tree structures. Boyd et al. [1] developed a genetic algorithm for the pipe dimensioning problem and used a penalty function to take both the minimum pressure and upstream pipe restrictions in account. The solutions were represented by a sequence of n integers, where n is the number of pipe segments (arcs) in the network, each integer indicating the index of the diameter to be chosen for a given pipeline.

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