Cartesian Ant Programming with adaptive node replacements
Cartesian Ant Programming with adaptive node replacements
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DOI：
10.1109/iwcia.2014.6988089
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Akira Hara;J. Kushida;Keita Fukuhara;T. Takahama
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文献类型：
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作者：
Akira Hara;J. Kushida;Keita Fukuhara;T. Takahama
蚁群优化算法（ACO）是一种基于群体的搜索方法。多个蚂蚁智能体通过信息素通信的正反馈机制搜索不同的解，并将搜索集中在好解周围。蚁群算法是解决组合优化问题的有效方法。将蚁群算法应用于自动程序设计是近年来的一种尝试。作为尝试之一，我们以前提出了笛卡尔蚂蚁编程（CAP）作为一个基于蚂蚁的自动编程方法。笛卡尔遗传规划（CGP）是一种用于图结构规划的进化优化方法。CAP将CGP中的图表示与ACO中的信息素通信相结合。蚁群算法可以优化程序原语、终端符号和功能符号之间的连接。CAP表现出比CGP更好的性能。然而，由于功能符号到节点的固定分配，相应符号的数量受到限制。因此，如果给定节点的数量不足以表示程序，则搜索性能变差。在本文中，为了解决这个问题，我们提出了CAP与自适应节点替换。该方法查找不用于表示程序的不必要的节点。然后，新的功能符号，这似乎是有用的构造良好的程序，分配给节点。通过这种方法，可以有效地利用给定的节点。为了检验我们的方法的有效性，我们将其应用到一个符号回归问题。CAP与自适应节点替换表现出更好的结果比传统的方法，CGP和CAP。
Ant Colony Optimization (ACO) is a swarm-based search method. Multiple ant agents search various solutions and their searches focus on around good solutions by positive feedback mechanism based on pheromone communication. ACO is effective for combinatorial optimization problems. The attempt of applying ACO to automatic programming has been studied in recent years. As one of the attempts, we have previously proposed Cartesian Ant Programming (CAP) as an ant-based automatic programming method. Cartesian Genetic Programming (CGP) is well-known as an evolutionary optimization method for graph-structural programs. CAP combines graph representations in CGP with pheromone communication in ACO. The connections of program primitives, terminal and functional symbols, can be optimized by ants. CAP showed better performance than CGP. However, quantities of respective symbols are limited due to the fixed assignments of functional symbols to nodes. Therefore, if the number of given nodes is not enough for representing program, the search performance becomes poor. In this paper, to solve the problem, we propose CAP with adaptive node replacements. This method finds unnecessary nodes which are not used for representing programs. Then, new functional symbols, which seems to be useful for constructing good programs, are assigned to the nodes. By this method, given nodes can be utilized efficiently. In order to examine the effectiveness of our method, we apply it to a symbolic regression problem. CAP with adaptive node replacements showed better results than conventional methods, CGP and CAP.