Paper at GECCO: Check.

by Amit


After the highly unsuccessful attempt at getting a paper published in GECCO 2008 with Yanhua Sun,  GECCO 2010 gives me the opportunity to scribble a “check” against GECCO in my mental “gotta be done” list.

My work with Prof. Deb, “Finding Multiple Solutions for Multimodal Optimization Problems Using a Multi-Objective Evolutionary Approach” will be presented at GECCO 2010 during the coming days.  In our work, we have proposed a new approach to multimodal optimization tasks using a Evolutionary Multi-objective approach. We have demonstrated the successful demonstration of our algorithm on high-dimensional unconstrained and constrained optimization problems.

The paper is not yet available on the ACM web pages. If you do not have access to the ACM, please email me for a PDF of the same. Here is the abstract:

In a multimodal optimization task, the main purpose is to find multiple optimal (global and local) solutions associated with a single objective function. Starting with the preselection method suggested in 1970, most of the existing evolutionary algorithms based methodologies employ variants of niching in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are de-emphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different and generic strategy in which a single-objective multimodal optimization problem in converted into a suitable bi-objective optimization problem so that all local and global optimal solutions become members of the resulting weak Pareto-optimal set. We solve up to 16-variable test-problems having as many as 48 optima and also demonstrate successful results on constrained multimodal test-problems, suggested for the first time. The concept of using multi-objective optimization for solving single-objective multimodal problems seems novel and interesting, and importantly opens further avenues for research.

Here is a demo showing our approach in action:

To learn more about Evolutionary Multimodal Optimization:

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