Created by W.Langdon from gp-bibliography.bib Revision:1.7866

- @InProceedings{Mizuno:2013:SICE,
- author = "Haruki Mizuno and Takashi Okamoto and Seiichi Koakutsu and Hironori Hirata",
- title = "A design method for the complex network growth model",
- booktitle = "Proceedings of SICE Annual Conference (SICE 2013)",
- year = "2013",
- month = "14-17 " # sep,
- pages = "571--576",
- keywords = "genetic algorithms, genetic programming, Complex Network, Network Growth Model, Network Design",
- URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6736203",
- abstract = "Many systems that can be modelled using network structures appear in various fields such as informatics, social science, economics, ecology, biology, and engineering. If these systems can be modelled as complex network systems, the complex network design method that finds a desired network structure can become one of strong tools in large-scale system designs. Conventional complex network design methods can only generate a topology of desired network. They can not present the network growth rule. If a network growth model which contains a network growth rule is obtained, then the designer can obtain not only the topology of the desired network but also a guideline for designing desired network. In this study, we propose a complex network growth model design method. In the proposed method, the complex network growth model is obtained by two methods. One is the weighted function optimisation method with the PSO. The weighted function consists of feature quantities. The other is the direct growth model design method with the GP. The growth model is optimised with respect to feature quantities. We try to generate a network growth model which resembles the well-known BA model on the clustering coefficient. We confirm the effectiveness of the proposed method through numerical experiments.",
- notes = "Also known as \cite{6736203}",
- }

Genetic Programming entries for Haruki Mizuno Takashi Okamoto Seiichi Koakutsu Hironori Hirata