Research Article
A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA
1: Choose Front (t) Random selection | 2: Choose P (t) According to an exponential distribution () | 3: u random(1) | 4: if u> then is the probability of crossover and mutation | 5: and are obtained by crossing over and | 6: else | 7: and are obtained by mutating and | 8: end if | 9: Choose Sub-Front (t) Random selection | 10: Choose P (t) According to an exponential distribution () | 11: u random(1) | 12: if u> then | 13: and are obtained by crossing over and | 14: else | 15: and are obtained by mutating and | 16: end if |
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