Evolving directed graphs with artificial bee colony algorithm
Evolving directed graphs with artificial bee colony algorithm
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DOI：
10.1109/isda.2014.7066282
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
Xianneng Li;Guangfei Yang;K. Hirasawa
中科院分区：
文献类型：
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作者：
Xianneng Li;Guangfei Yang;K. Hirasawa
人工蜂群（Artificial bee colony， ABC）算法是一种模拟蜂群智能觅食行为的较新的优化技术。将该方法应用于多个优化领域，证明了其有效的演化能力。本文首次将ABC算法应用于进化一种有向图染色体结构，该有向图染色体结构来源于一种新的基于图的进化算法——遗传网络规划（genetic network programming， GNP）。因此，探索了新的应用领域，可以有效地利用GNP的有向图进行建模。本文采用基于abc的进化策略，解决了在Tileworld基准测试平台下智能体行为的控制问题。将其性能与几种非常著名的计算机程序进化方法进行比较，包括具有交叉/突变、遗传规划（GP）和强化学习（RL）的标准GNP。
Artificial bee colony (ABC) algorithm is a relatively new optimization technique that simulates the intelligent foraging behavior of honey bee swarms. It has been applied to several optimization domains to show its efficient evolution ability. In this paper, ABC algorithm is applied for the first time to evolve a directed graph chromosome structure, which derived from a recent graph-based evolutionary algorithm called genetic network programming (GNP). Consequently, it is explored to new application domains which can be efficiently modeled by the directed graph of GNP. In this work, a problem of controlling the agents's behavior under a wellknown benchmark testbed called Tileworld are solved using the ABC-based evolution strategy. Its performance is compared with several very well-known methods for evolving computer programs, including standard GNP with crossover/mutation, genetic programming (GP) and reinforcement learning (RL).