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Comparing a coevolutionary genetic algorithm for multiobjective optimization
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Kraus, William F. Lohn, Jason D. Haith, Gary L. |
| Copyright Year | 2002 |
| Description | 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 finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front. |
| File Size | 428606 |
| Page Count | 6 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20030015725 |
| Archival Resource Key | ark:/13960/t8ff8qs6f |
| Language | English |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Computer Programming And Software Targets Populations Convexity Optimization Deception Genetic Algorithms Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
