Tuning of fuzzy rules with a real-codec genetic algorithm in car racing game
| Autor: | Motohide Umano, Noriyuki Fujimoto, Akifumi Ise |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
021103 operations research
Computer science business.industry 0211 other engineering and technologies 02 engineering and technology Fuzzy logic Competition (economics) Simple (abstract algebra) Genetic algorithm 0202 electrical engineering electronic engineering information engineering Codec 020201 artificial intelligence & image processing Point (geometry) Artificial intelligence business Car racing |
| Zdroj: | IFSA-SCIS |
| DOI: | 10.1109/ifsa-scis.2017.8023267 |
| Popis: | Car Racing Game is a competition of computer programs in IEEE CEC 2007, where two car agents compete with each other for taking way points in a two-dimensional plane. The agent can get information on itself, the other agent, and the current and next way points. In our previous research, we have evaluated agent states for the current and next way points with fuzzy rules from their speeds and the distances and angles to way points, to decide which way point to take. Then we have calculated the steering and the speed with fuzzy rules. We, however, have not won some programs in the competition. In this paper, we tune fuzzy rules with a real-coded genetic algorithm. The car agent tuned with a real-coded genetic algorithm for one of the best programs can win almost all programs. Moreover, it gets higher in performance than an ordinary simple genetic algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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