Creating chance by new interactive evolutionary computation: bipartite graph based interactive genetic algorithm

  • Authors:

  • Chao-Fu Hong;Hsiao-Fang Yang;Leuo-hong Wang;Mu-Hua Lin;Po-Wen Yang;Geng-Sian Lin

  • Affiliations:

  • Department of Information Management, Aletheia University, Taipei County, Taiwan (R.O.C.);Department of Management Information Systems, National Chengchi University, Taipei City, Taiwan (R.O.C);Department of Information Management, Aletheia University, Taipei County, Taiwan (R.O.C.);Department of Information Management, Aletheia University, Taipei County, Taiwan (R.O.C.);Department of Information Management, Aletheia University, Taipei County, Taiwan (R.O.C.);Department of Information Management, Aletheia University, Taipei County, Taiwan (R.O.C.)

  • Venue:
  • EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
  • Year:
  • 2006

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Abstract

In this paper, our model supplies designing environment that used the component network to identify the high score components and weak components which decrease the number of components to build a meaningful and easily analysis simple graph. Secondary analysis is the bipartite network as the method for formatting the structure or the structure knowledge. In this step the different clusters’ components could link each other, but the linkage could not connect the components on same cluster. Furthermore, some weak ties’ components or weak links are emerged by Bipartite Graph based Interactive Genetic Algorithm (BiGIGA) to assemble the creative products for customers. Finally, we investigated two significantly different cases. Case one, the customer did not change his preference, and the Wilcoxon test was used to evaluate the difference between IGA and BiGIGA. The results indicated that our model could correctly and directly capture the customer wanted. Case two, after the Wilcoxon test, it evidenced the lateral transmitting using triad closure extent the conceptual network, which could increase the weight of weak relation and retrieved a good product for the customer. The lateral transmitting did not present its convergent power on evolutionary design, but the lateral transmitting has illustrated that it could quickly discover the customer’s favorite value and recombined the creative product.