A hierarchical evolutionary approach to multi-objective optimization

This work describes a hierarchical evolutionary approach to Pareto-based multi-objective optimization. Using the SEAMO algorithm (a simple evolutionary algorithm for multi-objective optimization) as a basis, it demonstrates how it is possible to obtain a better spread of results if subpopulations of...

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Bibliographic Details
Main Author: Mumford, C.L.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Availability
Computer science
Computer science
control theory
systems
Concurrent computing
Content addressable storage
Degradation
Evolutionary computation
Exact sciences and technology
Genetics
Hardware
Modems
Scalability
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Summary:This work describes a hierarchical evolutionary approach to Pareto-based multi-objective optimization. Using the SEAMO algorithm (a simple evolutionary algorithm for multi-objective optimization) as a basis, it demonstrates how it is possible to obtain a better spread of results if subpopulations of various sizes are used in a simple hierarchical framework. Three alternative hierarchical models are tried and the results compared.
DOI:10.1109/CEC.2004.1331134