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Aickelin, Uwe; Dowsland, Kathryn (2008)
Languages: English
Types: Article
Subjects: Computer Science - Computational Engineering, Finance, and Science, Computer Science - Neural and Evolutionary Computing
During our earlier research, it was recognised that in order to be successful with an indirect genetic algorithm approach using a decoder, the decoder has to strike a balance between being an optimiser in its own right and finding feasible solutions. Previously this balance was achieved manually. Here we extend this by presenting an automated approach where the genetic algorithm itself, simultaneously to solving the problem, sets weights to balance the components out. Subsequently we were able to solve a complex and non-linear scheduling problem better than with a standard direct genetic algorithm implementation.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • Weights Initialised up to 50000
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