GA Demo: Multimodal Function Optimization

by Amit


In my last GA demo, we saw how the population of individuals in a Genetic Algorithm converged to the globally best part of the search space. What if we want all the best individuals i.e locally and globally best ones? The problem then becomes a multimodal optimization problem. See how it happens:

  • Uses the NSGA-II algorithm
  • Population size: 100, Generations: 150
  • Crossover: SBX Crossover
  • Mutation: Polynomial Mutation

Here is another demo with another function:

  • Uses the NSGA-II algorithm
  • Population size: 60, Generations: 100
  • Crossover: SBX Crossover
  • Mutation: Polynomial Mutation

The algorithm used here is based on our work  Deb, K., Saha, A., Finding Multiple Solutions for Multimodal Optimization Problems Using a Multi-Objective Evolutionary Approach (Accepted to be presented as full paper in GECCO-2010). (Let me know if you want to take a look at the paper).

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