Abstract:
In this paper we introduce a restart-CMA-evolution strategy, where the population size is increased for each restart (IPOP). By increasing the population size the search ...Show MoreMetadata
Abstract:
In this paper we introduce a restart-CMA-evolution strategy, where the population size is increased for each restart (IPOP). By increasing the population size the search characteristic becomes more global after each restart. The IPOP-CMA-ES is evaluated on the test suit of 25 functions designed for the special session on real-parameter optimization of CEC 2005. Its performance is compared to a local restart strategy with constant small population size. On unimodal functions the performance is similar. On multi-modal functions the local restart strategy significantly outperforms IPOP in 4 test cases whereas IPOP performs significantly better in 29 out of 60 tested cases.
Published in: 2005 IEEE Congress on Evolutionary Computation
Date of Conference: 02-05 September 2005
Date Added to IEEE Xplore: 12 December 2005
Print ISBN:0-7803-9363-5
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Population Size ,
- Evolutionary Strategy ,
- Increase In Population Size ,
- Small Population Size ,
- Constant Population Size ,
- Multimodal Functions ,
- Unimodal Functions ,
- Objective Function ,
- Function Tests ,
- Covariance Matrix ,
- Value Function ,
- Evaluation Of Function ,
- Evolutionary Algorithms ,
- Search Space ,
- Local Optimum ,
- Linear Transformation ,
- Successful Performance ,
- Performance Criteria ,
- Stopping Criterion ,
- Description Of Algorithm ,
- Benchmark Functions ,
- Adaptive Step Size ,
- Invariance Property ,
- Space Transformation ,
- Successful Run
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Population Size ,
- Evolutionary Strategy ,
- Increase In Population Size ,
- Small Population Size ,
- Constant Population Size ,
- Multimodal Functions ,
- Unimodal Functions ,
- Objective Function ,
- Function Tests ,
- Covariance Matrix ,
- Value Function ,
- Evaluation Of Function ,
- Evolutionary Algorithms ,
- Search Space ,
- Local Optimum ,
- Linear Transformation ,
- Successful Performance ,
- Performance Criteria ,
- Stopping Criterion ,
- Description Of Algorithm ,
- Benchmark Functions ,
- Adaptive Step Size ,
- Invariance Property ,
- Space Transformation ,
- Successful Run
