지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2003.4
- 수록면
- 816 - 821 (6page)
이용수
초록· 키워드
오류제보하기
Researchers have utilized artificial evolution techniques and learning techniques for studying the
interactions between learning and evolution. Adaptation in dynamic environments gains a significant
advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning
mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot
navigations. We support our method, based on (1+1) evolutionary strategy which produces changes during the
lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on
evolutionary neural controller for physical robots behaviors. We investigate the effects of learning in
evolutionary process by comparing the performance of the proposed realtime evolutionary learning method
with that of evolutionary method only. Also, we investigate an interactive evolutionary algorithm to overcome
the difficulties in evaluating complicated tasks.
interactions between learning and evolution. Adaptation in dynamic environments gains a significant
advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning
mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot
navigations. We support our method, based on (1+1) evolutionary strategy which produces changes during the
lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on
evolutionary neural controller for physical robots behaviors. We investigate the effects of learning in
evolutionary process by comparing the performance of the proposed realtime evolutionary learning method
with that of evolutionary method only. Also, we investigate an interactive evolutionary algorithm to overcome
the difficulties in evaluating complicated tasks.
목차
Abstract
1. 서론
2. 진화 로봇공학
3. 로봇 행위의 진화
4. 로봇 행위의 진화적 학습
5. 결론
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UCI(KEPA) : I410-ECN-0101-2009-550-014203489