By Youhua Zhang, Kunqi Liu, Gang Liu, Zhanfang Zhao (auth.), Zhihua Cai, Hengjian Tong, Zhuo Kang, Yong Liu (eds.)
This ebook constitutes the court cases of the fifth overseas Symposium on Computational Intelligence and clever platforms held in Wuhan, China, in October 2010.
Read or Download Computational Intelligence and Intelligent Systems: 5th International Symposium, ISICA 2010, Wuhan, China, October 22-24, 2010. Proceedings PDF
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Extra info for Computational Intelligence and Intelligent Systems: 5th International Symposium, ISICA 2010, Wuhan, China, October 22-24, 2010. Proceedings
Ultimately the optimal solution will appear in the population during the evolution. The coding of GEP combines the advantages of GA and GP, so that it can solve the coding explosion problem which is generated by GP, and its genetic operations are as simple as GA. According to Ferreira in the literature , GEP is faster than GP and GA 2 to 4 orders of magnitude. Due to its simple and efficient features, GEP is also a popular method of evolution modeling and widely used. 3 The Background of GEP_EDA Although GEP is simple and efficient, the GEP’s evolution process is random and we can only passively wait for the result of natural selection.
29–36, 2010. © Springer-Verlag Berlin Heidelberg 2010 30 Y. Han, H. Wang, and D. Li features, shows very complicated situation, and seems not to have relatively united rules. As a matter of fact, looking through these complex phenomena, we can see that the most natural matter of creative thinking is to construct possibility construction space which is sufficiently large and related to objects researched, and in this space to select, examine, at last get the new creative object. In other words, it is a process in which to construct new, currently non-existing objects in real world, by using information and knowledge owned by thinking subjects.
However many areas exist abundant complex systems (dynamic systems) and phenomena which are nonlinear and change over time, such as population growth, stock market fluctuations, changes in the number of sunspots, weather changes and so on. Nowadays Genetic Algorithm for Intelligent modeling has made great progress. Compared to traditional modeling, genetic algorithm has greater flexibility and higher speed and accuracy which the traditional algorithm can not match. More and more people applied the genetic algorithm in the intelligent modeling field, and they all achieved good results.
Computational Intelligence and Intelligent Systems: 5th International Symposium, ISICA 2010, Wuhan, China, October 22-24, 2010. Proceedings by Youhua Zhang, Kunqi Liu, Gang Liu, Zhanfang Zhao (auth.), Zhihua Cai, Hengjian Tong, Zhuo Kang, Yong Liu (eds.)