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Graph structure optimization of genetic network programming with ant colony mechanism in deterministic and stochastic environments

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 51卷 100581-000页

作者： Roshanzamir, Mohamad Palhang, Maziar Mirzaei, Abdolreza Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

Evolutionary Algorithms are of the most successful algorithms in solving various optimization problems. genetic network programming is one of the Evolutionary Algorithms with good capabilities in agent control problems. In this algorithm, the individuals' structure is a directed graph. Using this structure, it is possible to model the solution of many complex problems. However, in this algorithm, crossover and mutation operators repeatedly break the structures of individuals and make new ones. Although this can lead to better structures, it may break suitable structures in elite individuals. Meanwhile, in stochastic environments, each time an individual is evaluated, it leads to different fitness values. So, calculating the fitness value of individuals requires evaluating each individual several times. This extremely decreases the evolution process speed. In this paper, inspired by mechanisms of ant colony algorithm, a new method is proposed to prevent the algorithm from iteratively breaking down the structures of individuals. This method improves the performance of individuals from one generation to the next using a constructive process. Unlike generative process that the individuals are generated by combination of some others, in constructive process they are produced according to the experience of previous generations. Using this mechanism, we not only prevent breaking suitable structures but also can manage uncertainty in stochastic environments. Our proposed method is used to solve two agent control problems when the environment is deterministic or stochastic. The results show that the proposed algorithm has very high ability in creating an efficient decision making strategies especially in stochastic environments.

关键词： genetic network programming genetic programming Ant colony algorithm Agent control problems Deterministic environment Stochastic environment

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Combination of genetic network programming and knapsack problem to support record clustering on distributed databases

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 46卷 15-23页

作者： Wedashwara, Wirarama Mabu, Shingo Obayashi, Masanao Kuremoto, Takashi Yamaguchi Univ Grad Sch Sci & Engn Ube Yamaguchi 7558611 Japan

This research involves implementation of genetic network programming (GNP) and standard dynamic programming to solve the knapsack problem (KP) as a decision support system for record clustering in distributed databases. Fragment allocation with storage capacity limitation problem is a background of the proposed method. The problem of storage capacity is to distribute sets of fragments into several sites (clusters). Total amount of fragments in each site must not exceed the capacity of site, while the distribution process must keep the relation (similarity) between fragments within each site. The objective is to distribute big data to certain sites with the limited amount of capacities by considering the similarity of distributed data in each site. To solve this problem, GNP is used to extract rules from big data by considering characteristics (value ranges) of each attribute in a dataset. The proposed method also provides partial random rule extraction method in GNP to discover frequent patterns in a database for improving the clustering algorithm, especially for large data problems. The concept of KP is applied to the storage capacity problem and standard dynamic programming is used to distribute rules to each site by considering similarity (value) and data amount (weight) related to each rule to match the site capacities. From the simulation results, it is clarified that the proposed method shows some advantages over the conventional clustering algorithms, therefore, the proposed method provides a new clustering method with an additional storage capacity problem. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： genetic network programming Database clustering Knapsack problem Record clustering

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Implementation of genetic network programming and Knapsack Problem for Record Clustering on Distributed Database 53

Implementation of Genetic Network Programming and Knapsack P...

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SICE Annual Conference (SICE)

作者： Wedashwara, Wirarama Mabu, Shingo Obayashi, Masanao Kuremoto, Takashi Yamaguchi Univ Grad Sch Sci & Engn Yamaguchi Japan

ISBN: (纸本)9784907764456

This research involves implementation of genetic network programming (GNP) and knapsack problem (KP) to solve record clustering on distributed databases. The objective is to distribute big data to certain sites with the limited amount of capacities by considering the similarity of distributed data in each site. GNP is used to extract rules from big data by considering characteristics (value ranges) of each attribute in a dataset. KP is used to distribute rules to each site by considering similarity (value) and data amount (weight) related to each rule to match the site capacities.

关键词： genetic network programming Database Clustering Knapsack Problem Record Clustering

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Evolving graph-based chromosome by means of variable size genetic network programming with binomial distribution

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2013年第4期8卷 348-356页

作者： Li, Bing Li, Xianneng Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

genetic network programming (GNP) is a graph-based evolutionary algorithm with fixed size, which has been proven to solve complicated problems efficiently and effectively. In this paper, variable size genetic network programming (GNPvs) with binomial distribution has been proposed, which will change the size of the individuals and obtain their optimal size during evolution. The proposed method will select the number of nodes to move from one parent GNP to another parent GNP during crossover to implement the new feature of GNP. The probability of selecting the number of nodes to move satisfies a binomial distribution. The proposed method can keep the effectiveness of crossover, improve the performance of GNP, and find the optimal size of the individuals. The well-known testbed Tileworld is used to show the numerical results in the simulations. (c) 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： variable size genetic network programming crossover binomial distribution Tileworld

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Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第16期40卷 6311-6320页

作者： Mabu, Shingo Hirasawa, Kotaro Obayashi, Masanao Kuremoto, Takashi Yamaguchi Univ Grad Sch Sci & Engn Ube Yamaguchi 7558611 Japan Waseda Univ Informat Prod & Syst Res Ctr Kitakyushu Fukuoka 8080135 Japan

Evolutionary computation generally aims to create the optimal individual which represents optimal action rules when it is applied to agent systems. genetic network programming (GNP) has been proposed as one of the graph-based evolutionary computations in order to create optimal individuals. GNP with rule accumulation is an extended algorithm of GNP, which extracts a large number of rules throughout the generations and stores them in rule pools, which is different from general evolutionary computations. Concretely, the individuals of GNP with rule accumulation are regarded as evolving rule generators in the training phase and the generated rules in the rule pools are actually used for decision making. In this paper, GNP with rule accumulation is enhanced in terms of its rule extraction and classification abilities for generating stock trading signals considering up and down trends and occurrence frequency of specific buying/selling timing. A large number of buying and selling rules are extracted by the individuals evolved in the training period. Then, a unique classification mechanism is used to appropriately determine whether to buy or sell stocks based on the extracted rules. In the testing simulations, the stock trading is carried out using the extracted rules and it is confirmed that the rule-based trading model shows higher profits than the conventional individual-based trading model. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Evolutionary computation genetic network programming Rule extraction Stock trading Technical analysis

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K-CUT CROSSOVER USING GRAPH THEORY IN genetic network programming

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第2期9卷 641-650页

作者： Murata, Hiroaki Koshino, Makoto Kimura, Haruhiko Kanazawa Univ Inst Nat & Environm Technol Kanazawa Ishikawa 9201192 Japan Kanazawa Univ Grad Sch Nat Sci Kanazawa Ishikawa 9201192 Japan Ishikawa Natl Coll Technol Dept Elect & Informat Engn Tsubata Ishikawa 9290392 Japan

In this study, we focus on genetic network programming (GNP) which is the graph-based evolutionary algorithm. Similar to genetic Algorithm (GA) and genetic programming (GP), GNP applies genetic operators to an individual, which is represented by a directed graph, in order to solve a given problem. GNP is usually applied to automatic generation of programs which control a mobile robot. Since the crossover exchanges a sub-graph of parent individuals, a selection of a sub-graph is an important factor. Some selection methods are proposed in previous work. However, the selection method based on the graph theory is not proposed even though the individual is represented by a graph. In this study, we propose a k-cut crossover based on the graph theory. The proposed k-cut crossover selects a sub-graph by using a minimum k-cut algorithm which finds a minimum graph partition on weighted graph. We applied the GNP with the k-cut crossover to the automatic generation of programs in the tileworld, and the experimental result shows the advantage of the k-cut crossover.

关键词： K-cut crossover operator genetic network programming GNP Minimum cut problem Frequency of edge usage Graph theory

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Usage of Frequently Used Node in Variable Size genetic network programming

Usage of Frequently Used Node in Variable Size Genetic Netwo...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Li, Bing Yu, Shanqing Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Fukuoka Japan

ISBN: (纸本)9781479906529

This paper describes a kind of replacement mechanism which improves the generalization ability of Variable Size genetic network programming (GNPvs). GNPvs is an extension of genetic network programming (GNP), which has changeable number of nodes. Inspired by the theory of Evolution by Gene Duplication, the non-frequently used nodes are replaced with the frequently used nodes in the proposed method, which can make the individual survive under the selection pressure as usual, but eventually might accumulate mutations that produce new features of individuals for adapting new environments. The effectiveness of the proposed method is verified by comparing with the performance of GNPvs and GNP on a well-known dynamic multi-agent test bed - Tileworld.

关键词： genetic network programming Variable size Generalization Tile-world

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A Learning Classifier System Based on genetic network programming

A Learning Classifier System Based on Genetic Network Progra...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Li, Xianneng Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781479906529

Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named eXtended rule-based genetic network programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.

关键词： learning classifier systems genetic network programming niching fitness sharing reinforcement learning

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NP4G: network programming for Generalization

NP4G: Network Programming for Generalization

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Intelligent Systems Conference (IntelliSys)

作者： Hara, Shoichiro Watanabe, Yuji Nagoya City Univ Nagoya Aichi Japan

ISBN: (纸本)9783031477201;9783031477218

In recent years, the development of Artificial Intelligence systems using neural network has been remarkable. However, this method has low explainability and is illogical. To solve this, there is an automatic programming method based on inductive inference. However, this method has also the problem of low versatility. In this study, we propose NP4G: network programming for Generalization, which can automatically generate programs by inductive inference. Because the proposed method can realize "sequence", "selection", and "iteration" in programming and can satisfy the conditions of the structured program theorem, it is expected that NP4G is a method that automatically acquires any programs by inductive inference. As an example, we automatically construct a bitwise NOT operation program from several training data by generalization using NP4G. Although NP4G only randomly selects and connects nodes, by adjusting the number of nodes and the number of phase of "Phased Learning", we show the bitwise NOT operation programs are acquired in a comparatively short time and at a rate of about 7 in 10 running. The source code of NP4G is available on GitHub as a public repository.

关键词： Inductive Inference Automatic programming Knowledge Acquisition genetic programming genetic network programming

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network intrusion detection system using ANFIS classifier

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SOFT COMPUTING 2023年第3期27卷 1629-1638页

作者： Sajith, P. J. Nagarajan, G. Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

As network security had really become a basic issue, there is been a lot of improvement in designs for past years. Among several designs, IDS is one of the greatest hits. It has all of the stores of being that the presence of inadequacy and the dubious considered the impedances make fuzzy developments sensible for such structures. Fuzzy plans are not regularly versatile and do not have the ability to make models solely subject to the goal structure's model data. So, this paper aims in using Adaptive Neuro-Fuzzy Inference System (ANFIS) as a classifier for classifying the networks as malicious categories (Probe, DoS, U2R, R2L) and normal on DARPA 1999 database and are evaluated with other models like Fuzzy GNP, HHO in which experimental results show that ANFIS show better performance because it joins the upsides of both ANN and fuzzy deduction frameworks including the capacity to catch the nonlinear design of interaction, variation ability, and fast learning limit.

关键词： Adaptive neuro-fuzzy inference system Artificial neural network Intrusion detection system genetic network programming

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