Achieving Long-Term Progress in Competitive Co-Evolution
| Autor: | Stefano Nolfi, Luca Simione |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Current generation
Computer science arm-races Distributed computing 05 social sciences Mobile robot 02 engineering and technology Competitor analysis long-term progress 050105 experimental psychology Term (time) competitive co-evolution 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences |
| Zdroj: | Proceedings of IEEE Symposium Series on Computational Intelligence, edited by D. Foegel and P. Bonissone (Eds.), pp. 855–862, 2017 info:cnr-pdr/source/autori:Simione, Luca; Nolfi, Stefano/titolo:Achieving Long-Term Progress in Competitive Co-Evolution/titolo_volume:Proceedings of IEEE Symposium Series on Computational Intelligence/curatori_volume:D. Foegel and P. Bonissone (Eds.)/editore:/anno:2017 SSCI |
| Popis: | We illustrate how co-evolutionary experiments involving simulated predator and prey robots can lead to long-term global progress, i.e. can produce robots displaying progressively better performance against both competitors of current and previous generations. This is obtained by exposing evolving robots to well-differentiated competitors, by preserving individuals displaying good performance against hard to handle competitors, and by discarding opportunistic individuals that perform poorly against the other competitors of the current generation. The accumulation of variations producing general progress for more than 50,000 generations leads to the evolution of sophisticated behavioral capabilities and enable evolved robots to outperform robots evolved with simpler methods. |
| Databáze: | OpenAIRE |
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