Titlebook: Advances of Evolutionary Computation: Methods and Operators; Erik Cuevas,Margarita Arimatea Díaz Cortés,Diego A Book 2016 Springer Interna

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https://doi.org/10.1007/978-3-319-28503-0Computational Intelligence; Evolutionary Computation; Swarm algorithms; Metaheuristics; Artificial Intel

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Oleksiy Shulika,Igor Sukhoivanovs based on the simulation of the cooperative behavior of social-spiders. In the presented algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and fe

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Cutaneous Pathology of the Head and Neck,f the found-so-far elements and exploration of the search space. Inspired by natural phenomena, researchers have developed many successful evolutionary algorithms. In their original versions, such approaches define operators that mimic the way in which nature solves complex problems overlooking the

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Comprehensive Rehabilitation of Oral Cancer,n animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, that exhibit a variety of behaviors including swarming about a food source, milling around a central location or migrating over large distances in aligned groups. These collective behaviors are oft

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Salvage Treatment for Recurrent Oral Cancer,he collective behavior of social insects or animals. Several SI algorithms have been proposed to solve a wide range of complex optimization applications. Although such methods are designed to meet the requirements of generic optimization problems, no single algorithm can solve all problems competiti

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Christina Mimikos,Sudhir Nair,David Cohanxpensive objective functions. Recently, Evolutionary Algorithms (EAs) are gaining popularity for solving complex problems encountered in many engineering disciplines. They are found to be more robust and effective to locate global optima compared to classical optimization methods. However, one diffi