XIII. 推奨値


この章では、遺伝的アルゴリズムを実行する際に、目安となるいくつかの基本的な推奨値を与えたいと思います。 これらの推奨値は非常に一般的です。 たぶん、特定の問題にたいして自分のGAで実験したいとおもうでしょう。 なぜなら、今日、いかなる問題に対してのGAのパラメータが述べられた一般的な理論がありません。

This chapter should give you some basic recommendations if you have decided to implement your genetic algorithm. These recommendations are very general. Probably you will want to experiment with your own GA for specific problem, because today there is no general theory which would describe parameters of GA for any problem.


Recommendations are often results of some empiric studies of GAs, which were often performed only on binary encoding.








Applications of GA

Genetic algorithms has been used for difficult problems (such as NP-hard problems), for machine learning and also for evolving simple programs. They have been also used for some art, for evolving pictures and music.

Advantage of GAs is in their parallelism. GA is travelling in a search space with more individuals (and with genotype rather than phenotype) so they are less likely to get stuck in a local extreme like some other methods.

They are also easy to implement. Once you have some GA, you just have to write new chromosome (just one object) to solve another problem. With the same encoding you just change the fitness function and it is all.On the other hand, choosing encoding and fitness function can be difficult.

Disadvantage of GAs is in their computational time. They can be slower than some other methods. But with todays computers it is not so big problem.

To get an idea about problems solved by GA, here is a short list of some applications:

More information can be found through links in the appendix.

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(c) Marek Obitko, 1998
Japanese translation (c) Ishii Manabu, 1999
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