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| Title: | Co-optimization algorithms |
| Author (s): | Service, Travis C. Tauritz, Daniel R. |
| Department/Lab Affiliations: | Computer Science Energy Research and Development Center Intelligent Systems Center |
| Keywords: | Gradient ascent algorithms |
| Subject Terms: | Coevolution. Simulated annealing (Mathematics) |
| Issue Date: | 2008-07 |
| Publisher: | Association for Computing Machinery |
| Citation: | Service, Travis C., and Daniel R. Tauritz. Co-optimization algorithms, Proceedings of the 10th annual conference on Genetic and evolutionary computation (July 2008): 387-388. |
| Abstract: | While coevolution has many parallels to natural evolution, methods other than those based on evolutionary principles may be used in the interactive fitness setting. In this paper we present a generalization of coevolution to co-optimization which allows arbitrary black-box function optimization techniques to be used in a coevolutionary like manner. We find that the co-optimization versions of gradient ascent and simulated annealing are capable of outperforming the canonical coevolutionary algorithm. We also hypothesize that techniques which employ non-population based selection mechanisms are less sensitive to disengagement. |
| Type: | Article - Conference proceedings text |
| In Title: | Proceedings of the 10th Annual Conference on Genetic and 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. Pre-print: author can archive; Post-print: author can archive; FULL COPYRIGHT INFORMATION: |
| Publisher URL: | |
| Link to this page: |
| title | Co-optimization algorithms |
| contributor.author | Service, Travis C. |
| contributor.author | Tauritz, Daniel R. |
| contributor.deptlab | Computer Science |
| contributor.deptlab | Energy Research and Development Center |
| contributor.deptlab | Intelligent Systems Center |
| subject | Gradient ascent |
| subject | algorithms |
| subject.LCSH | Coevolution. |
| subject.LCSH | Simulated annealing (Mathematics) |
| date.issued | 2008-07 |
| publisher | Association for Computing Machinery |
| identifier.citation | Service, Travis C., and Daniel R. Tauritz. Co-optimization algorithms, Proceedings of the 10th annual conference on Genetic and evolutionary computation (July 2008): 387-388. |
| identifier.pub.URI | |
| description.abstract | While coevolution has many parallels to natural evolution, methods other than those based on evolutionary principles may be used in the interactive fitness setting. In this paper we present a generalization of coevolution to co-optimization which allows arbitrary black-box function optimization techniques to be used in a coevolutionary like manner. We find that the co-optimization versions of gradient ascent and simulated annealing are capable of outperforming the canonical coevolutionary algorithm. We also hypothesize that techniques which employ non-population based selection mechanisms are less sensitive to disengagement. |
| type | Article - Conference proceedings |
| type.DCMIType | text |
| type.status | Postprint |
| relation.isPartOf | Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation |
| 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 | Pre-print: author can archive; Post-print: author can archive; |
| rights.URI | |
| date.available | 2008-11-20T22:37:57Z |
| identifier.persist.URI |