Multiobjective meta level optimization of a load balancing evolutionary algorithm

Authors:
David J. Caswell;Gary B. Lamont
Affiliations:
Department of Electrical and Computer Engineering, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH;Department of Electrical and Computer Engineering, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH
Venue:
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Year:
2003

Citing 4
Cited 0

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
How to solve it: modern heuristics

How to solve it: modern heuristics
Performance Comparison of Processor Scheduling Strategies in a Distributed-Memory Multicomputer System

IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

For any optimization algorithm tuning the parameters is necessary for effective and efficient optimization. We use a meta-level evolutionary algorithm for optimizing the effectiveness and efficiency of a load-balancing evolutionary algorithm. We show that the generated parameters perform statistically better than a standard set of parameters and analyze the importance of selecting a good region on the Pareto Front for this type of optimization.