By Yong Liu (auth.), Lishan Kang, Zhihua Cai, Xuesong Yan, Yong Liu (eds.)
This booklet constitutes the refereed lawsuits of the 3rd foreign Symposium on Intelligence Computation and functions, ISICA 2008, held in Wuhan, China, in December 2008.
The ninety three revised complete papers have been rigorously reviewed and chosen from approximately seven-hundred submissions. The papers are equipped in topical sections on computational intelligence, evolutionary computation, evolutionary multi-objective and dynamic optimization, evolutionary studying platforms, neural networks, category and popularity, bioinformatics and bioengineering, evolutionary info mining and data discovery, clever GIS and keep an eye on, thought of clever computation, combinational and numerical optimization, in addition to real-world applications.
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Extra info for Advances in Computation and Intelligence: Third International Symposium, ISICA 2008 Wuhan, China, December 19-21, 2008 Proceedings
In this paper, we only discuss the situation when p 1 and k l=0. ＝ ＝ 2 Algorithm DSSSEA The essence to solve fucntion optimization problem in dynamic environments is to detect the change of condition in the progress of seraching and to make the algorithm to catch up with the change of condition. It has been said that to find an effective way to solve dynamic function optimization problem, that is to say, improving diversity of individuals can make the algorithm to adapt to the change of condition more quickly.
It is very encouraging that OSDEP is capable of performing as well as or better than the better one of IFEP, FEP and CEP for all the test functions. This is achieved through a minimal change to the existing FEP and CEP. No prior knowledge or any complicated operators were use, and also no additional parameter was used either. g. “step sizes”) and optimum search direction in a robust search algorithm. It is that Cauchy mutations played a major role in the population in the early stages of evolution since the distance between the current search points and the global optimum was relatively large on average in the early stages, hence Cauchy mutation performed better.
53640 26 M. Yang and J. Guan Table 3. Comparison between DCCM-EA and FEP, where “Mean Best” and “Std Dev” stand for the mean best values and the standard deviation respectively. Each function is evaluated independently for 50 runs. 14 apparent that DCCM-EA performs better than FEP for the rest part of the 23 benchmark functions. 4 Conclusions This paper introduced a dynamic-clone and chaos-mutation evolutionary algorithm (DCCM-EA), which employs dynamic clone and chaos mutation methods, for function optimization.
Advances in Computation and Intelligence: Third International Symposium, ISICA 2008 Wuhan, China, December 19-21, 2008 Proceedings by Yong Liu (auth.), Lishan Kang, Zhihua Cai, Xuesong Yan, Yong Liu (eds.)