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Titlebook: Applications of Evolutionary Computing; EvoWorkshops 2008: E Mario Giacobini,Anthony Brabazon,Shengxiang Yang Conference proceedings 2008 S

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Encyclopedia of Machine Learningr terminals to LERs; 2) placement of LERs; and 3) selection of the actually installed LSRs and their links, while distributing the traffic over the network. Results show that our techniques attain the optimal solution, as given by GNU solver - ., effectively with minimum computational burden.
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Adaptive Real-Time Dynamic Programmingndom sampling of the space, which reduces the need to compute the performance of parameter settings in advance. Results show a high accuracy of the estimations and a simplification in the method to select the configurations we have to simulate to optimize the MLP.
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Architecture Performance Prediction Using Evolutionary Artificial Neural Networksndom sampling of the space, which reduces the need to compute the performance of parameter settings in advance. Results show a high accuracy of the estimations and a simplification in the method to select the configurations we have to simulate to optimize the MLP.
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Adaptive Real-Time Dynamic Programmingmpared with a version fixed for the FAP problem of the population-based incremental learning (PBIL) algorithm. The results show that SS obtains better frequency plannings than PBIL for all the experiments performed.
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Agent-Based Computational Modelsr its reconfiguration to meet changing conditions. We examine the issues of noise during our evolutionary runs, show how this was overcome and illustrate our system with a simple proof-of-concept task that shows how the same mechanism of control works for progressive developmental stages (canalisation) or adaptable control (homoeostasis).
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Agent-Based Computational Modelsin the process of synthesizing image features. Experiments concerning navigation in indoor environment indicate that the evolved controller performs quite well despite very limited human intervention in the design phase.
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New Research in Nature Inspired Algorithms for Mobility Management in GSM Networksconsists in optimizing the number and location of paging cells to find the lowest location management cost. There is a need to develop techniques capable of operating with this complexity and used to solve a wide range of location management scenarios. Nature inspired algorithms are useful in this c
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