Abstract:
We present four abstract evolutionary algorithms for multi-objective optimization and theoretical results that characterize their convergence behavior. Thanks to these re...Show MoreMetadata
Abstract:
We present four abstract evolutionary algorithms for multi-objective optimization and theoretical results that characterize their convergence behavior. Thanks to these results it is easy to verify whether or not a particular instantiation of these abstract evolutionary algorithms offers the desired limit behavior. Several examples are given.
Published in: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)
Date of Conference: 16-19 July 2000
Date Added to IEEE Xplore: 06 August 2002
Print ISBN:0-7803-6375-2
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Evolutionary Algorithms ,
- Partial Order ,
- Infinity ,
- Markov Chain ,
- Cardinality ,
- Selection Procedure ,
- Search Space ,
- Positive Matrix ,
- Adaptive Algorithm ,
- Finite Set ,
- Set Of Elements ,
- Finite Time ,
- Linear Order ,
- Working Population ,
- Transition Probability Matrix ,
- Set Membership ,
- Probability 1 ,
- Construction Algorithm ,
- Crossover Operator ,
- Positive Probability ,
- Tournament Selection
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Evolutionary Algorithms ,
- Partial Order ,
- Infinity ,
- Markov Chain ,
- Cardinality ,
- Selection Procedure ,
- Search Space ,
- Positive Matrix ,
- Adaptive Algorithm ,
- Finite Set ,
- Set Of Elements ,
- Finite Time ,
- Linear Order ,
- Working Population ,
- Transition Probability Matrix ,
- Set Membership ,
- Probability 1 ,
- Construction Algorithm ,
- Crossover Operator ,
- Positive Probability ,
- Tournament Selection
