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Comprehensive Parent Selection-based Genetic Algorithm

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Abstract

During the past few years, many variations of genetic algorithm (GA) have been proposed. These algorithms have been successfully used to solve problems in different disciplines such as engineering, business, science, and networking etc. Real world optimization problems are divided into two categories: (1) single objective, and (2) multi-objective. Genetic algorithms have key advantages over other optimization techniques to deal with multi-objective optimization problems. One of the most popular techniques of GA to obtain the Pareto-optimal set of solutions for multi-objective problems is the non-dominated sorting genetic algorithm- II (NSGA-II). In this paper, we propose a variant of NSGA-II that we call the comprehensive parent selection-based genetic algorithm (CPSGA). The proposed strategy uses the information of all the individuals to generate new offspring from the selected parents. This strategy ensures diversity to discourage premature convergence. CPSGA is tested using the standard ZDT benchmark problems and the performance metrics taken from the literature. Moreover, the results produced are compared with the original NSGA-II algorithm. The results show that the proposed approach is a viable alternative to solve multi-objective optimization problems.

Keywords

  • Genetic Algorithm
  • Particle Swarm Optimization
  • Pareto Front
  • Generational Distance
  • Premature Convergence

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Hamid Ali .

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© 2012 Springer-Verlag London

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Ali, H., Khan, F.A. (2012). Comprehensive Parent Selection-based Genetic Algorithm. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_9

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  • DOI: https://doi.org/10.1007/978-1-4471-4739-8_9

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