@inproceedings{mori_adaptation_2000,
	title = {Adaptation to a {Dynamical} {Environment} by {Means} of the {Environment} {Identifying} {Genetic} {Algorithm}},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01299867},
	abstract = {Adaptation to dynamic environments is an important application of genetic algorithms (GAs). However, there are many difficulties to apply the GA to dynamic environments. Especially, in online environments, the GA’s defects become remarkable because individuals should be evaluated in the real world. In this paper, we proposes a novel approach to such an online adaptation called the environment identifying Genetic Algorithm (EIGA). The EIGA achieves the online adaptation and identification of the environments simultaneously by the parallel technique and reduce the number of fitness evaluations in the real world by utilizing the identified environment. The thermodynamical selection rule is also utilized to maintain diversity. Computer simulation is carried out by taking an Nk-landscape problem as an example.},
	booktitle = {{IEEE} {Industrial} {Electronics} {Conference}, {IECON} 2000},
	author = {Mori, N. and Kude, T. and Matsumoto, K.},
	year = {2000},
	keywords = {Adaptation, Benchmarking, Computer simulation, Dynamic environments, Genetic algorithms, Optimization, Scheduling}
}

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