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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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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description 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_str_mv 10.1109/CEC.2004.1331134
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identifier ISBN: 9780780385153
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source IEEE Electronic Library (IEL) Conference Proceedings
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
title A hierarchical evolutionary approach to multi-objective optimization
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