Genetic Network Programming with Automatically Generated Macro Nodes of Variable Size

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  • Mabu Shingo
    Graduate School of Information, Production, and Systems, Waseda University
  • Hatakeyama Hiroyuki
    Graduate School of Information, Production, and Systems, Waseda University
  • Nakagoe Hiroshi
    Graduate School of Information, Production, and Systems, Waseda University
  • Hirasawa Kotaro
    Graduate School of Information, Production, and Systems, Waseda University
  • Furuzuki Takayuki
    Graduate School of Information, Production, and Systems, Waseda University

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  • プログラムサイズ可変型マクロノードつき遺伝的ネットワークプログラミング
  • プログラムサイズ カヘンガタ マクロノードツキ イデンテキ ネットワーク プログラミング

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Abstract

Recently, Genetic Network Programming (GNP) has been proposed as one of the evolutionary algorithms. It represents its solutions as directed graph structures and the distinguished abilities have been shown. However, when we apply GNP to complex problems like the real world one, GNP must have robustness against the changes of environments and evolve quickly. Therefore, we introduced Automatically Generated Macro Nodes (AGMs) to GNP (GNP with AGMs). Actually GNP with AGMs has shown higher performances than the conventional GNP in terms of the fitness and the speed of evolution. In this paper, a new mechanism, AGMs with variable size, is introduced to GNP. Conventional AGMs have the fixed number of nodes and they evolve using only genetic operations, while a new method allows AGM to add nodes by necessity and delete nodes which do not contribute to the evolution of the AGM. The proposed GNP with AGMs of variable size is expected to evolve effectively and efficiently when it is applied to agent systems and also expected to make better behavior sequences of agents more easily than the conventional GNP algorithm. In the simulations, the proposed and conventional methods are applied to a tileworld problem and they are compared with each other. From the results, GNP with AGMs of variable size shows better fitness than GNP with AGMs of fixed size and the conventional GNP when adapting ten different environments.

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