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Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series)

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Genetic algorithms (GAs) are randomized search and optimization techniques guided by the principles of evolution and natural genetics; they have a large amount of implicit parallelism. GAs perform multimodal search in complex landscapes and provide near-optimal solutions for objective or tness func tion of an optimization problem. They have applications in elds as diverse as pattern recognition, image processing, neural networks, machine learning, jobshop scheduling and VLSI design, to mention just a few.

Traditionally, GAs were designed to solve problems with an objective to optimize only a single criterion. However, many real-life problems involve mul tiple conicting measures of performance, or objectives, which need simulta neous optimization. Optimal performance according to one objective, if such an optimum exists, often implies unacceptably low performance in one or more of the other objective dimensions, creating the need for a compromise to be reached. A suitable set of solutions, called the Pareto-optimal set, to such problems is one where none of the solutions can be further improved on any one objective without degrading it in another. In recent times, there has been a spurt of activities in exploiting the signi cantly powerful search capa bility of GAs for multiobjective optimization, leading to the development of several algorithms belonging to the class of multiobjective genetic algorithms (MOGAs).
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