Comparing a coevolutionary genetic algorithm for multiobjective optimization

We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population rep...

Full description

Saved in:
Bibliographic Details
Main Authors: Lohn, J.D., Kraus, W.F., Haith, G.L.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Automatic testing
Encoding
Evolutionary computation
Genetic algorithms
Hardware
Organisms
Performance evaluation
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA yields poor coverage across the Pareto front, yet finds a solution that dominates all the solutions produced by the eight other algorithms.
DOI:10.1109/CEC.2002.1004406