Titlebook: Nature Inspired Cooperative Strategies for Optimization (NICSO 2013); Learning, Optimizati German Terrazas,Fernando E. B. Otero,Antonio D.

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Escaping Local Optima via Parallelization and Migration,other and on different threads. The overall goal is to develop a population-based algorithm capable to escape from local optima. In doing so, we used complex trap functions, and we provide experimental answers to some crucial implementation decision problems. The obtained results show the robustness

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Part-of-Speech Tagging Using Evolutionary Computation, of a text with labels that designate the appropriate parts-of-speech. The approach proposed in this paper divides the problem into two tasks: a learning task and an optimization task. Algorithms from the field of evolutionary computation were adopted to tackle each of those tasks. We emphasize the

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Nonlinear Optimization in Landscapes with Planar Regions,, given by the slope, necessary to guide the search is insufficient. In such case, a common solution can be a change in the metaheuristic’s parameters in order to attain a optimal balance between the exploration and exploitation. In this paper, we propose a criterion to determine when a flat region

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Optimizing Neighbourhood Distances for a Variant of Fully-Informed Particle Swarm Algorithm,ms worse. This is to be expected in light of theoretical results like No free lunch theorem. It is desirable, therefore, to have an automatic method of constructing algorithms tuned for solving specific problems and classes of problems. We offer a variant of Fully-Informed Particle Swarm Optimizatio

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