By Minghui Jiang, Yongqing Zhao, Yi Shen (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)
The 3 quantity set LNCS 5551/5552/5553 constitutes the refereed lawsuits of the sixth foreign Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in may well 2009.
The 409 revised papers provided have been conscientiously reviewed and chosen from a complete of 1.235 submissions. The papers are equipped in 20 topical sections on theoretical research, balance, time-delay neural networks, desktop studying, neural modeling, choice making platforms, fuzzy platforms and fuzzy neural networks, aid vector machines and kernel tools, genetic algorithms, clustering and class, trend attractiveness, clever regulate, optimization, robotics, photo processing, sign processing, biomedical functions, fault analysis, telecommunication, sensor community and transportation structures, in addition to applications.
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Extra info for Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part III
Di( k ) (the distance between ai and a j ) denotes the k-th distance ascending order among all other individuals 's distance to ai . The function g (k ) given in Definition 2 is a decreasing function. The specific meaning of function g (k ) has direct influence on evolutionary algorithm’s ultimate result. Generally, the influencing strength of other individuals on considering individual is directly related to their distance. The distance between ai and a j is represented as dij . Taking into account that the influence value of evaluation function is small if dij is large, following two rules are derived.
Then the population diversity entropy is defined as H = −∑ P lg( Pi ) . Proi =1 vided the number of population |A| is constant, from the definition of diversity entropy, it can be concluded that the value of Pi is close if the individuals can be uniform distributed in each divided subset . That is to say a high uncertainty of the distribution of individuals means that the population has a high entropy value H. Therefore, in the multi-objective evolutionary algorithms, great entropy value corresponds to well-diversity of population in decision space.
Liu 5 Conclusion Numerical experiments demonstrate that the proposed crowding evaluation algorithm can achieve better results in diversity maintenance. It can be derived from the method of entropy metrics that greater entropy value corresponds to better diversity after implementing diversity maintenance strategy. Two-dimensional and multidimensional numerical experiment results demonstrate that the proposed strategy shows better performance in entropy reduction and losses of uniform distribution than traditional diversity maintenance strategies.
Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part III by Minghui Jiang, Yongqing Zhao, Yi Shen (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)