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Hybrid Genetic Algorithm: An Optimization Tool
Abstract
Real coded Genetic Algorithms (GAs) are the most effective and popular techniques for solving continuous optimization problems. In the recent past, researchers used the Laplace Crossover (LX) and Power Mutation (PM) in the GA cycle (namely LX-PM) efficiently for solving both constrained and unconstrained optimization problems. In this chapter, a local search technique, namely Quadratic Approximation (QA) is discussed. QA is hybridized with LX-PM in order to improve its efficiency and efficacy. The generated hybrid system is named H-LX-PM. The supremacy of H-LX-PM over LX-PM is validated through a test bed of 22 unconstrained and 15 constrained typical benchmark problems. In the later part of this chapter, a few applications of GA in networking optimization are highlighted as the scope for future research.
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