| dc.creator | Walter I. | |
| dc.creator | Gomide F. | |
| dc.date | 2008 | |
| dc.date | 2015-06-30T19:22:42Z | |
| dc.date | 2015-11-26T15:38:23Z | |
| dc.date | 2015-06-30T19:22:42Z | |
| dc.date | 2015-11-26T15:38:23Z | |
| dc.date.accessioned | 2018-03-28T22:46:53Z | |
| dc.date.available | 2018-03-28T22:46:53Z | |
| dc.identifier | 9781424416134 | |
| dc.identifier | 2008 3rd International Workshop On Genetic And Evolving Fuzzy Systems, Gefs. , v. , n. , p. 53 - 58, 2008. | |
| dc.identifier | | |
| dc.identifier | 10.1109/GEFS.2008.4484567 | |
| dc.identifier | http://www.scopus.com/inward/record.url?eid=2-s2.0-50149114682&partnerID=40&md5=745c912cb6fa423223b56cfd5127b82f | |
| dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/106003 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/106003 | |
| dc.identifier | 2-s2.0-50149114682 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1263891 | |
| dc.description | Following the development of online markets, trading practices as dynamic pricing, online auctions and exchanges have become relevant to a variety of markets. In this paper we suggest a machine learning approach to And a suitable bidding strategy for an auction participant using information commonly available in online auction settings. We take the electricity auction as the main application example, due to its importance as an experimental instance of the suggested approach. In previous works we evolved successful fuzzy bidding strategies. Here we introduce a revolutionary algorithm to study how the evolving strategies react to each other in a more dynamic environment. By enabling a fuzzy system to learn trough an evolutionary algorithm one expects to find effective and transparent bidding strategies. By adopting a coevolutionary approach a more realistic representation of the agents participating in an auction based electricity market allows the evolutionary bidding strategies interact. The results show that the coevolutionary approach can improve agents profits at the cost of increasing system hourly price paid by demand. © 2008 IEEE. | |
| dc.description | | |
| dc.description | | |
| dc.description | 53 | |
| dc.description | 58 | |
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| dc.description | Phelps, S.G., (2007) Evolutionary Mechanism Design, , PhD thesis, Univeristy of Liverpool, UK, July | |
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| dc.description | I. Walter and F. Gomide. Design of coordination strategies in multiagent systems via genetic fuzzy systems. Soft Computing, 10(10):903-915, Aug. 2006. Special Issue: New Trends in the Design of Fuzzy SystemsI. Walter and F. Gomide. Genetic fuzzy systems to evolve coordination strategies in multiagent systems. International Journal of Intelligent Systems. 22(9):971-991, 2007. Special Issue on Genetic Fuzzy SystemsWidjaja, M., Sugianto, L.F., Morrison, R.E., Fuzzy model of generator bidding system in competitive electricity markets (2001) 10th IEEE International Conference on Fuzzy Systems, 3, pp. 1396-1399. , Melbourne, Australia, Dec, IEEE | |
| dc.description | Xiong, G., Hashiyama, T., Okuma, S., An evolutionary computation for supplier bidding strategy in electricity auction market (2002) IEEE Power Engineering Society Transmission and Distribution Conference, 1, pp. 83-88. , Yokohama, Japan, IEEE | |
| dc.language | en | |
| dc.publisher | | |
| dc.relation | 2008 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS | |
| dc.rights | fechado | |
| dc.source | Scopus | |
| dc.title | Coevolutionary Fuzzy Multiagent Bidding Strategies In Competitive Electricity Markets | |
| dc.type | Actas de congresos | |
