### 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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I am a research student focusing on GA for combinatorial optimization problems. I would like to receive few of your publications specially on multimodal function optimization. Can you help me to get few unimodal functions with results for comparing my work?… Pls help me.

Thanks

expecting a reply

Hello, your comment is a little confusing. Are you looking for test unimodal functions? If yes, then there are a number of them in the standard literature: Ellipsoidal function, Rosenbrock’s function,etc which you can find in papers.