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June 02, 2005
Miki(1999)
M. Miki, T. Hiroyasu, M. Kaneko, K. Hatanaka. "A Parallel Genetic
Algorithm with Distributed Environment Scheme". IEEE International
Conference on Systems, Man and Cybernetics, Vol.1, pp.695-700, 1999.
GA関連のサーベイの続き．
同時に複数のGAを走らせながら，メタGAな方法でもってして最適解を探索するアプローチ．
関心としてはかなり近いところにあるといえる．
ただ，少し足りない．
分散GAと呼ばれる方法で，ポピュレーションを分割して，それぞれにおいてGAを走らせると．
で，これが従来のいわゆるGAをアウトパフォームすることがいえていると．
ただ，最適なパラメータの設定をどうするのかが問題であると．
It is well known that distributed GAs with multiple
subpopulations or with isolation by distance can maintain diversity in
the evolving population. Tanese [12] showed the ability of finding
fitter individuals than the traditional one. Belding [13] extended
Tanase's work on distributed GA to the different fitness functions in
order to determine whether her results were specific to the Tanase
functions. He showed that distributed GA outperformed canonical GA for
the functions which are not easy for canonical GA. From these results,
we can conclude that distributed GA outperforms canonical GA in some
sense. However, the optimal-parameter setting problem still remains in
distributed GAs.
Miki(1999) pp.696
それを解決するものとして，本研究があると．
To overcome these problems, we propose a new distributed
GA with a distributed environment scheme. In this scheme, a whole
population is divided into several subpopulations, and the GA
parameters such as the mutation rate and the crossover rate in each
subpopulation are different from each other. Themigration operation is
performed similarly as the conventional distributed GA.
Miki(1999) pp.698-699
ただ，こういう保証のしかたっていうのはどうだろうか．
もうすこしオートマティズムを求めたいところではある．
The tuning of GA parameters is not necessary with DEGA-----
since many combinations of such GA parameters are used in many
subpopulations. Consequently, it can be expected that a global optimum
can be easily obtained without any preexperiments with DEGA.
Miki(1999) pp.699
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