Presentation | 2000/3/21 Adaptive GP-Automata for the maze problem Hiroaki Kataoka, Tomoharu Nagao, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Ganerally, it is difficult for Genetic Programming; GP to solve problem which requires several different output to the same input signal of an agent. This paper, we improve GP-Automata, a modified version of GP previously proposed by Ashlock for such problems, and propose a new method named Adaptive GP-Automata; AGPA. Since AGPA automatically obtains the adequate total number of states of the agent for solving a given task successfully through evolutionary iterations, complex and laborious trial and error for tuning it is not necessary in AGPA. Several experiments to investigate the performance of AGPA were executed using maze problems, and their results suggested the advantage of AGPA compared with the original version of GP-Automata. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Genetic Programming / GP-Automata / maze problem |
Paper # | AI99-92,KBSE99-51 |
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Conference Information | |
Committee | AI |
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Conference Date | 2000/3/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Adaptive GP-Automata for the maze problem |
Sub Title (in English) | |
Keyword(1) | Genetic Programming |
Keyword(2) | GP-Automata |
Keyword(3) | maze problem |
1st Author's Name | Hiroaki Kataoka |
1st Author's Affiliation | Imaging Science and Engineering Laboratory, Tokyo Institute of Technology() |
2nd Author's Name | Tomoharu Nagao |
2nd Author's Affiliation | Imaging Science and Engineering Laboratory, Tokyo Institute of Technology |
Date | 2000/3/21 |
Paper # | AI99-92,KBSE99-51 |
Volume (vol) | vol.99 |
Number (no) | 717 |
Page | pp.pp.- |
#Pages | 8 |
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