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| Title: | Greedy population sizing for evolutionary algorithms | |
| Author (s): | Smorodkina, Ekaterina Tauritz, Daniel R. | |
| Department/Lab Affiliations: | Computer Science | |
| Keywords: | Evolutionary Algorithms Greedy Population Sizing automated population size tuning method | |
| Issue Date: | 2007 | |
| Publisher: | Intitute of Electrical and Electronics Engineers, Inc. | |
| Citation: | Smorodkina, Ekaterina., and Tauritz, Daniel. "Greedy Population Sizing for Evolutionary Algorithms." Proceedings of CEC 2007 - IEEE Congress on Evolutionary Computation, (2007). | |
| Abstract: | The number of parameters that need to be man ually tuned to achieve good performance of Evolutionary Algorithms and the dependency of the parameters on each other make this potentially robust and efficient computational method very time consuming and difficult to use. This paper introduces a Greedy Population Sizing method for Evolutionary Algo rithms (GPS-EA), an automated population size tuning method that does not require any population size related parameters to be specified or manually tuned a priori. Theoretical analysis of the number of function evaluations needed by the GPS EA to produce good solutions is provided. We also perform an empirical comparison of the performance of the GPS-EA to the performance of an EA with a manually tuned fixed population size. Both theoretical and empirical results show that using GPS-EA eliminates the need for manually tuning the population size parameter, while finding good solutions. This comes at the price of using twice as many function evaluations as needed by the EA with an optimal fixed population size; this, in practice, is a low price considering the amount of time and effort it takes to find this optimal population size manually. | |
| Type: | Article - Journal text | |
| In Title: | Proceedings of CEC 2007 - IEEE Congress on Evolutionary Computation | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Greedy population sizing for evolutionary algorithms | |
| contributor.author | Smorodkina, Ekaterina | |
| contributor.author | Tauritz, Daniel R. | |
| contributor.deptlab | Computer Science | |
| subject | Evolutionary Algorithms | |
| subject | Greedy Population Sizing | |
| subject | automated population size tuning method | |
| date.issued | 2007 | |
| publisher | Intitute of Electrical and Electronics Engineers, Inc. | |
| identifier.citation | Smorodkina, Ekaterina., and Tauritz, Daniel. "Greedy Population Sizing for Evolutionary Algorithms." Proceedings of CEC 2007 - IEEE Congress on Evolutionary Computation, (2007). | |
| identifier.pub.URI | ||
| description.abstract | The number of parameters that need to be man ually tuned to achieve good performance of Evolutionary Algorithms and the dependency of the parameters on each other make this potentially robust and efficient computational method very time consuming and difficult to use. This paper introduces a Greedy Population Sizing method for Evolutionary Algo rithms (GPS-EA), an automated population size tuning method that does not require any population size related parameters to be specified or manually tuned a priori. Theoretical analysis of the number of function evaluations needed by the GPS EA to produce good solutions is provided. We also perform an empirical comparison of the performance of the GPS-EA to the performance of an EA with a manually tuned fixed population size. Both theoretical and empirical results show that using GPS-EA eliminates the need for manually tuning the population size parameter, while finding good solutions. This comes at the price of using twice as many function evaluations as needed by the EA with an optimal fixed population size; this, in practice, is a low price considering the amount of time and effort it takes to find this optimal population size manually. | |
| type | Article - Journal | |
| type.DCMIType | text | |
| type.status | Postprint | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| relation.isPartOf | Proceedings of CEC 2007 - IEEE Congress on Evolutionary Computation | |
| date.accessioned | 2008-04-11T20:06:19Z | |
| date.available | 2008-04-11T20:06:21Z | |
| identifier.persist.URI | ||
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