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Erschienen in:
2020 | OriginalPaper | Buchkapitel
11. GA with Repeated Crossover for Rectifying Optimization Problems
verfasst von : Mayank Jha, Sunita Singhal
Erschienen in: Business Intelligence for Enterprise Internet of Things
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Abstract
There have been various genetic algorithms (GAs) that have been initiated for the purpose of solving optimization issues in the course of research purposes in optimization. Because of the variability in the features of various optimization issues, none of these algorithms are capable of displaying a more robust performance. The differentiating aim of every optimizing issue potentially makes it more difficult. The success of the GA is dependent on the search operators. In this research, we have proposed the GA that basically works on until we obtain an effective offspring. To determine the performance of the algorithms, we have compared our algorithm with some well-known single-objective optimization problems and analyzed the results. The experimental evaluation indicated that the algorithm arrives quicker than its counterparts to the optimal solution. Also, the results produced were better in terms of the objective value, thus exhibiting a superior performance in terms of both runtime and fitness value.