Download Evolutionary Computation in Combinatorial Optimization: 7th by Enrique Alba, Gabriel Luque (auth.), Carlos Cotta, Jano van PDF

By Enrique Alba, Gabriel Luque (auth.), Carlos Cotta, Jano van Hemert (eds.)

This ebook constitutes the refereed complaints of the seventh eu convention on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2007, held in Valencia, Spain in April 2007.

The 21 revised complete papers offered have been conscientiously reviewed and chosen from eighty one submissions. The papers conceal evolutionary algorithms in addition to quite a few different metaheuristics, like scatter seek, tabu seek, memetic algorithms, variable local seek, grasping randomized adaptive seek approaches, ant colony optimization, and particle swarm optimization algorithms.

The papers are in particular devoted to the appliance of evolutionary computation and similar tips on how to combinatorial optimization difficulties and canopy any factor of metaheuristic for combinatorial optimization. They care for representations, heuristics, research of challenge buildings, and comparisons of algorithms. The checklist of studied combinatorial optimization difficulties contains well-liked examples like graph coloring, knapsack difficulties, the touring shop clerk challenge, scheduling, graph matching, in addition to particular real-world problems.

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Additional info for Evolutionary Computation in Combinatorial Optimization: 7th European Conference, EvoCOP 2007, Valencia, Spain, April 11-13, 2007. Proceedings

Example text

This A Probabilistic Beam Search Approach to the SCSP 41 is done as follows. First, we calculate for each st ∈ C a heuristic value η(st ) as follows: ⎛ t ⎞−1 η(st ) ← ⎝ |s | ν r st (i) | st (1)st (2) . . st (i − 1) ⎠ , (4) i=1 where ν r (α | s) is the rank of the weight ν(α | s) which the LA-WMM(l) heuristic assigns to the extension α of string s (see Section 3). The rank of extending string s by symbol α is obtained by sorting all possible extensions of string s with respect to their LA-WMM(l) weights in descending order.

Note that we use algorithm PBS in a multi-start fashion, that is, given a CPU time limit we apply algorithm PBS over and over again until the CPU limit is reached. The best solution found, denoted by sbsf , is recorded. In fact, this solution is one of the input parameters of algorithm PBS. It is used to exclude partial solutions whose lower bound value exceeds |sbsf | from further consideration. 2 for compiling the software. Our experimental results were obtained on a PC with an AMD64X2 4400 processor and 4 Gb of memory.

The metaheuristic and the branch and bound obtain approximately the same solutions’ quality for all the coefficients. However, for lower CQ’s the latter is slightly better, whereas for the larger CQ’s the former is better. With 20 activities, the behavior of B&BN is much worse. Even the priority rule outperforms it. We can also see that the metaheuristic algorithm is better than the multi-pass algorithm plus IP in all combinations of d and CQ. 34 F. Ballestín Table 1. 85% % opt. sol. 83% Table 2.

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