Runtime analysis of non-elitist populations: from classical optimisation to partial information
Although widely applied in optimisation, relatively little has been proven rigorously about the role and behaviour of populations in randomised search processes. This paper presents a new method to prove upper bounds on the expected optimisation time of population-based randomised search heuristics...
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| Format: | Article |
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Springer
2016
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| Online Access: | https://eprints.nottingham.ac.uk/31142/ |
| _version_ | 1848794135331340288 |
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| author | Dang, Duc-Cuong Lehre, Per Kristian |
| author_facet | Dang, Duc-Cuong Lehre, Per Kristian |
| author_sort | Dang, Duc-Cuong |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Although widely applied in optimisation, relatively little has been proven rigorously about the role and behaviour of populations in randomised search processes. This paper presents a new method to prove upper bounds on the expected optimisation time of population-based randomised search heuristics that use non-elitist selection mechanisms and unary variation operators. Our results follow from a detailed drift analysis of the population dynamics in these heuristics. This analysis shows that the optimisation time depends on the relationship between the strength of the selective pressure and the degree of variation introduced by the variation operator. Given limited variation, a surprisingly weak selective pressure suffices to optimise many functions in expected polynomial time. We derive upper bounds on the expected optimisation time of non-elitist Evolutionary Algorithms (EA) using various selection mechanisms, including fitness proportionate selection. We show that EAs using fitness proportionate selection can optimise standard benchmark functions in expected polynomial time given a sufficiently low mutation rate.
As a second contribution, we consider an optimisation scenario with partial information, where fitness values of solutions are only partially available. We prove that non-elitist EAs under a set of specific conditions can optimise benchmark functions in expected polynomial time, even when vanishingly little information about the fitness values of individual solutions or populations is available. To our knowledge, this is the first runtime analysis of randomised search heuristics under partial information. |
| first_indexed | 2025-11-14T19:11:23Z |
| format | Article |
| id | nottingham-31142 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:11:23Z |
| publishDate | 2016 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-311422020-05-04T17:37:32Z https://eprints.nottingham.ac.uk/31142/ Runtime analysis of non-elitist populations: from classical optimisation to partial information Dang, Duc-Cuong Lehre, Per Kristian Although widely applied in optimisation, relatively little has been proven rigorously about the role and behaviour of populations in randomised search processes. This paper presents a new method to prove upper bounds on the expected optimisation time of population-based randomised search heuristics that use non-elitist selection mechanisms and unary variation operators. Our results follow from a detailed drift analysis of the population dynamics in these heuristics. This analysis shows that the optimisation time depends on the relationship between the strength of the selective pressure and the degree of variation introduced by the variation operator. Given limited variation, a surprisingly weak selective pressure suffices to optimise many functions in expected polynomial time. We derive upper bounds on the expected optimisation time of non-elitist Evolutionary Algorithms (EA) using various selection mechanisms, including fitness proportionate selection. We show that EAs using fitness proportionate selection can optimise standard benchmark functions in expected polynomial time given a sufficiently low mutation rate. As a second contribution, we consider an optimisation scenario with partial information, where fitness values of solutions are only partially available. We prove that non-elitist EAs under a set of specific conditions can optimise benchmark functions in expected polynomial time, even when vanishingly little information about the fitness values of individual solutions or populations is available. To our knowledge, this is the first runtime analysis of randomised search heuristics under partial information. Springer 2016-02-09 Article PeerReviewed Dang, Duc-Cuong and Lehre, Per Kristian (2016) Runtime analysis of non-elitist populations: from classical optimisation to partial information. Algorithmica, 75 (3). pp. 428-461. ISSN 1432-0541 Runtime Drift analysis Evolutionary Algorithms Non-elitism Fitness-levels Partial evaluation http://link.springer.com/article/10.1007%2Fs00453-015-0103-x doi:10.1007/s00453-015-0103-x doi:10.1007/s00453-015-0103-x |
| spellingShingle | Runtime Drift analysis Evolutionary Algorithms Non-elitism Fitness-levels Partial evaluation Dang, Duc-Cuong Lehre, Per Kristian Runtime analysis of non-elitist populations: from classical optimisation to partial information |
| title | Runtime analysis of non-elitist populations: from classical optimisation to partial information |
| title_full | Runtime analysis of non-elitist populations: from classical optimisation to partial information |
| title_fullStr | Runtime analysis of non-elitist populations: from classical optimisation to partial information |
| title_full_unstemmed | Runtime analysis of non-elitist populations: from classical optimisation to partial information |
| title_short | Runtime analysis of non-elitist populations: from classical optimisation to partial information |
| title_sort | runtime analysis of non-elitist populations: from classical optimisation to partial information |
| topic | Runtime Drift analysis Evolutionary Algorithms Non-elitism Fitness-levels Partial evaluation |
| url | https://eprints.nottingham.ac.uk/31142/ https://eprints.nottingham.ac.uk/31142/ https://eprints.nottingham.ac.uk/31142/ |