Applications of Evolutionary Computing: EvoWorkshops 2002: by Peter Cowling, Graham Kendall, Eric Soubeiga (auth.), PDF

By Peter Cowling, Graham Kendall, Eric Soubeiga (auth.), Stefano Cagnoni, Jens Gottlieb, Emma Hart, Martin Middendorf, Günther R. Raidl (eds.)

ISBN-10: 3540434321

ISBN-13: 9783540434320

This publication constitutes the refereed lawsuits of 3 workshops at the software of evolutionary programming and algorithms in quite a few domain names; those workshops have been held along side the fifth eu convention on Genetic Programming, EuroGP 2002, in Kinsale, eire, in April 2002.
The 33 revised complete papers offered have been rigorously reviewed and chosen by means of the respective software committees. in response to the 3 workshops EvoCOP, EvoIASP, and EvoSTIM/EvoPLAN, the papers are equipped in topical sections on combinatorial optimization difficulties; photo research and sign processing; and scheduling, timetabling, and AI planning.

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Extra resources for Applications of Evolutionary Computing: EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN Kinsale, Ireland, April 3–4, 2002 Proceedings

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In the problem we are facing, allocation of parallel related tasks onto processors, we can address the search by looking at the processors or the task priorities, or even by looking at both at the same time. In this work we propose different EAs using different chromosome representations by means of decoders and permutations. To guide our search towards schedules with optimal makespan, three variants were considered. In the first variant processor dispatching priorities are encoded. In the second one, tasks priority lists are encoded, and in the third scheme a bipartite chromosome represents both processor dispatching priorities and tasks priority lists.

St , 1 ≤ k < |s| : – τ (ck , ck+1 ) ← τ (ck , ck+1 ) + αt Qf (s)G τ (ck , ck+1 ) – ∀y : τ (ck , y) ← τ (ck , y) − αt Qf (s)PT y| prefk (s) G τ (ck , y) Hence any connection (i, j) used in the construction of a solution is reinforced by an amount αt Qf (s)G τ (i, j) , and any connection considered during the construction, has its pheromone values evaporated by an amount αt Qf (s)PT j| prefk (s) G τ (i, j) . Note, that if the solutions are allowed to contain loops, a connection may be updated more than once for the same solution.

In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 11–32. McGraw Hill, London, UK, 1999. 2. G. Di Caro and M. Dorigo. AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research, 9:317–365, 1998. 3. D. P. Bertsekas. Nonlinear Programming. Athena Scientific, Belmont, MA, 1995. 4. R. Y. Rubinstein. The cross-entropy method for combinatorial and continuous optimization. Methodology and Computing in Applied Probability, 1(2):127–190, 1999.

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Applications of Evolutionary Computing: EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN Kinsale, Ireland, April 3–4, 2002 Proceedings by Peter Cowling, Graham Kendall, Eric Soubeiga (auth.), Stefano Cagnoni, Jens Gottlieb, Emma Hart, Martin Middendorf, Günther R. Raidl (eds.)


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