Self-adaptation of mutation rates in non-elitist populations
The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experimental results indicate that self-adaptation, where...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2016
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| Online Access: | https://eprints.nottingham.ac.uk/34365/ |
| _version_ | 1848794835654279168 |
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| author | Lehre, Per Kristian Dang, Duc-Cuong |
| author_facet | Lehre, Per Kristian Dang, Duc-Cuong |
| author_sort | Lehre, Per Kristian |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation. However, results in discrete optimisation have been less conclusive. Furthermore, a rigorous runtime analysis that explains how self adaptation can lead to asymptotic speedups has been missing. This paper provides the first such analysis for discrete, population-based EAs. We apply level-based analysis to show how a self-adaptive EA is capable of fine-tuning its mutation rate, leading to exponential speedups over EAs using fixed mutation rates. |
| first_indexed | 2025-11-14T19:22:30Z |
| format | Conference or Workshop Item |
| id | nottingham-34365 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:22:30Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-343652020-05-04T17:49:55Z https://eprints.nottingham.ac.uk/34365/ Self-adaptation of mutation rates in non-elitist populations Lehre, Per Kristian Dang, Duc-Cuong The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation. However, results in discrete optimisation have been less conclusive. Furthermore, a rigorous runtime analysis that explains how self adaptation can lead to asymptotic speedups has been missing. This paper provides the first such analysis for discrete, population-based EAs. We apply level-based analysis to show how a self-adaptive EA is capable of fine-tuning its mutation rate, leading to exponential speedups over EAs using fixed mutation rates. 2016-05-27 Conference or Workshop Item PeerReviewed Lehre, Per Kristian and Dang, Duc-Cuong (2016) Self-adaptation of mutation rates in non-elitist populations. In: 14th International Conference on Parallel Problem Solving from Nature, 17-21 Sept 2016, Edinburgh, UK. (In Press) |
| spellingShingle | Lehre, Per Kristian Dang, Duc-Cuong Self-adaptation of mutation rates in non-elitist populations |
| title | Self-adaptation of mutation rates in non-elitist populations |
| title_full | Self-adaptation of mutation rates in non-elitist populations |
| title_fullStr | Self-adaptation of mutation rates in non-elitist populations |
| title_full_unstemmed | Self-adaptation of mutation rates in non-elitist populations |
| title_short | Self-adaptation of mutation rates in non-elitist populations |
| title_sort | self-adaptation of mutation rates in non-elitist populations |
| url | https://eprints.nottingham.ac.uk/34365/ |