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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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https://doi.org/10.1057/9781137395023dea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program. The test results demonstrate that the proposed idea can be used with success to solve the approximation problems.
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Democracy and Citizenship in ScandinaviaThis paper compares the performance of GAs and PSOs in evolving weights of a recurrent neural network. The algorithms are tested on multiple network topologies. Both algorithms produce successful networks. The GA is more successful evolving larger networks and the PSO is more successful on smaller networks.
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Comparison of Genetic Algorithm and Particle Swarm Optimizer When Evolving a Recurrent Neural NetworThis paper compares the performance of GAs and PSOs in evolving weights of a recurrent neural network. The algorithms are tested on multiple network topologies. Both algorithms produce successful networks. The GA is more successful evolving larger networks and the PSO is more successful on smaller networks.
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https://doi.org/10.1007/3-540-45105-6agents; algorithm; algorithms; evolution; evolutionary computation; genetic algorithm; genetic algorithms;
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978-3-540-40602-0Springer-Verlag Berlin Heidelberg 2003
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0302-9743 Overview: 978-3-540-40602-0978-3-540-45105-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
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