Document Type

Article

Date

12-20-2005

Embargo Period

3-8-2013

Keywords

Electrical Engineering and Computer Science

Disciplines

Electrical and Computer Engineering

Description/Abstract

A new evolutionary multi-objective crowding algorithm (EMOCA) is evaluated using nine benchmark multiobjective optimization problems, and shown to produce non-dominated solutions with significant diversity, outperforming state-of-the-art multi-objective evolutionary algorithms viz., Non-dominated Sorting Genetic Algorithm – II (NSGA-II), Strength Pareto Evolutionary algorithm II (SPEA-II) and Pareto Archived Evolution Strategy (PAES) on most of the test problems. The key new approach in EMOCA is to use a diversity-emphasizing probabilistic approach in determining whether an offspring individual is considered in the replacement selection phase, along with the use of a non-domination ranking scheme. This approach appears to provide a useful compromise between the two concerns of dominance and diversity in the evolving population.

Additional Information

Copyright 2012 2nd Indian International Conference on Artificial Intelligence. This article may be downloaded for personal use only. Any other use requires prior permission of the author and publisher 2nd Indian International Conference on Artificial Intelligence.

The article may be found at

http://www.informatik.uni-trier.de/~ley/db/conf/iicai/iicai2005.html

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Local Input

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