PERFORMANCE AMELIORATION OF GENETIC ALGORITHM
Empirical Investigations and Policy Implications
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As Genetic Algorithms (GA) are widely applied in multiple scientific domains as an optimization strategy, the need for a high performing genetic algorithm is indispensable. This book provides a detailed survey of several performance improvement techniques in GA and presents a simple and adaptive multi-population genetic algorithm called Nomadic Genetic Algorithm (NGA) for performance amelioration of GA. It is applied for three diverse problems and compared with Standard GA (SGA) and other Multi- population GA. Though GA is adept at solving optimization problems, it is not directly suitable to ...
As Genetic Algorithms (GA) are widely applied in multiple scientific domains as an optimization strategy, the need for a high performing genetic algorithm is indispensable. This book provides a detailed survey of several performance improvement techniques in GA and presents a simple and adaptive multi-population genetic algorithm called Nomadic Genetic Algorithm (NGA) for performance amelioration of GA. It is applied for three diverse problems and compared with Standard GA (SGA) and other Multi- population GA. Though GA is adept at solving optimization problems, it is not directly suitable to solve problems with user specific constraints. A survey of techniques to handle such constrained optimization problems as well as a simpler and natural solution to handle them is provided in the form of a biologically inspired genetic operator namely Gene Silencing . It is implemented for two diverse problems and compared with other techniques. Thus this book illuminates the probable areas of research in Genetic Algorithm for researchers in this field as well as in the algorithm domain to choose a simple and natural way to solve problems.
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