Accepted:
December 2004
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/vol11/raghuw01/ | © Copyright 2005 | |||
| Volume 11 | Received: Accepted: |
November 2004 December 2004 |
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Survey on multiobjective evolutionary andreal coded genetic algorithms
M.M. Raghuwanshi and O.G. Kakde |
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| Abstract | |
| Evolutionary Algorithm (EA) possesses several characteristics that are desirable to solve real-world optimization problems up to a required level of satisfaction. Multiobjective Evolutionary Algorithms (MOEAs) are designed with regard to two common goals, fast and reliable convergence to the Paretoset and a good distribution of solutions along the front. Virtually each algorithm represents a unique combination of specific techniques to achieve these goals. Handling continuous search space with binary coded genetic algorithm has several difficulties. Real coded genetic algorithm represents parameters without coding, which makes representation of the solutions very close to the natural formulation of many problems. In real coded GA (RCGA) recombination and mutation operators are designed to work with real parameters. This survey gives state-of–the-art of multiobjective evolutionary algorithms and real coded genetic algorithms. | |
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Multimedia Links (none) Reference Links (none) Citation Reference M.M. Raghuwanshi and O.G. Kakde 2005, Survey on multiobjective evolutionary andreal coded genetic algorithms, Complexity International, Volume 11, Paper ID: raghuw01, URL: http://www.complexity.org.au/vol11/raghuw01/ |
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