Learning and evolution of genetic network programming with knowledge transfer
Learning and evolution of genetic network programming with knowledge transfer
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DOI：
10.1109/cec.2014.6900315
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发表时间：
2014-07
期刊：
影响因子：
--
通讯作者：
Xianneng Li;Wen He;K. Hirasawa
中科院分区：
文献类型：
--
作者：
Xianneng Li;Wen He;K. Hirasawa
传统的进化算法一般是从零开始进化的，也就是说，是随机进化的。然而，这会耗费大量的计算，而且很容易导致进化的不稳定性。为了解决上述问题，本文提出了一种通过引入知识转移能力来提高进化效率的新方法--基于图的遗传网络规划(GNP)。该方法的基本思想称为GNP-KT，它通过在学习分类器系统(LCS)方面从源域中发现抽象的决策规则来形成知识，并在将GNP应用于目标领域时自适应地将知识重用为建议。提出了一种基于强化学习(RL)的方法，将知识从源域自动传递到目标域，最终使GNP-KT获得更好的初始性能和最终的适应值。在实际移动机器人控制问题中的实验结果证实了GNP-KT方法比传统方法的优越性。
Traditional evolutionary algorithms (EAs) generally starts evolution from scratch, in other words, randomly. However, this is computationally consuming, and can easily cause the instability of evolution. In order to solve the above problems, this paper describes a new method to improve the evolution efficiency of a recently proposed graph-based EA - genetic network programming (GNP) - by introducing knowledge transfer ability. The basic concept of the proposed method, named GNP-KT, arises from two steps: First, it formulates the knowledge by discovering abstract decision-making rules from source domains in a learning classifier system (LCS) aspect; Second, the knowledge is adaptively reused as advice when applying GNP to a target domain. A reinforcement learning (RL)-based method is proposed to automatically transfer knowledge from source domain to target domain, which eventually allows GNP-KT to result in better initial performance and final fitness values. The experimental results in a real mobile robot control problem confirm the superiority of GNP-KT over traditional methods.