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Titlebook: Genetic and Evolutionary Computation - GECCO 2003; Genetic and Evolutio Erick Cantú-Paz,James A. Foster,Julian Miller Conference proceeding

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發(fā)表于 2025-3-23 12:28:01 | 只看該作者
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發(fā)表于 2025-3-23 17:44:30 | 只看該作者
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發(fā)表于 2025-3-23 19:45:09 | 只看該作者
https://doi.org/10.1007/978-3-322-81613-9s appear to be optimal depending on the algorithm run-time. The performance of the .-. Ant System (.AS) on the University Course Timetabling Problem (UCTP) — a type of constraint satisfaction problem — is used as an example. The parameters taken into consideration include the type of the local searc
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發(fā)表于 2025-3-23 23:45:27 | 只看該作者
Democracies and the Populist Challengeained) flocking algorithm and the other a relatively un-constrained system in which the behavior of agents is governed by evolved computer programs. We describe the systems in detail, document the emergence of collective behavior, and argue that these systems present new opportunities for the study
15#
發(fā)表于 2025-3-24 04:19:28 | 只看該作者
Participation and Representation, We use predators-prey pursuit problem to verify our hypothesis that relatively complex social behavior may emerge from simple, implicit, locally defined, and therefore — robust and highly-scalable interactions between the predator agents. We propose a proximity perception model for the predator age
16#
發(fā)表于 2025-3-24 07:35:52 | 只看該作者
17#
發(fā)表于 2025-3-24 14:45:29 | 只看該作者
https://doi.org/10.1057/9781137013200le preference for mating with those males who possess that trait. This paper reports on the results of a simulation performed to test the hypothesis that female preference for mating with co-operative males can lead to an increase in the proportions of males in a population who co-operate with femal
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發(fā)表于 2025-3-24 17:07:12 | 只看該作者
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發(fā)表于 2025-3-24 22:47:12 | 只看該作者
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發(fā)表于 2025-3-24 23:52:27 | 只看該作者
https://doi.org/10.1007/978-3-319-92690-2s of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniformly distributed in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three benchmark functions were used to test the new method. The
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