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genetic network programming for Automatic Program Generation

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2005年第4期9卷 430-436页

作者： Mabu, Shingo Hirasawa, Kotaro Matsuya, Yuko Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan Kyushu Univ Grad Sch Informat Sci & Elect Engn Higashi Ku Fukuoka Fukuoka 8128581 Japan

In this paper, a recently proposed Evolutionary Computation method called genetic network programming (GNP) is applied to generate programs such as Boolean functions. GNP is an extension of genetic Algorithm (GA) and genetic programming (GP). It has a directed graph structure as gene and can search for solutions effectively. GNP has been mainly applied to dynamic problems and has shown better performances compared to GP. However, its application to static problems has not yet been studied well. Thus in this paper, GNP is applied to generate programs as its extension to solving static problems. In order to apply GNP to generating static problems, we introduced a new element, memory. In the proposed method, a GNP individual consists of a directed graph and a memory, while one in conventional GNP consists only of a directed graph. In the simulations, GNP succeeded in solving Even-n-Parity problem and Mirror Symmetry problem.

关键词： evolutionary computation genetic network programming memory architecture even-n-parity problem mirror symmetry problem

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Fuzzy genetic network programming with Reinforcement Learning for Mobile Robot Navigation

Fuzzy Genetic Network Programming with Reinforcement Learnin...

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

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

ISBN: (纸本)9781457706530

This paper proposes Fuzzy genetic network programming with Reinforcement Learning (Fuzzy GNP-RL). This method integrates fuzzy logic to the conventional GNP-RL. The new part of the proposed method is fuzzy judgment nodes. Fuzzy GNP-RL provides flexibility to determine the appropriate next node by the probabilistic transition instead of that by the threshold values on GNP-RL. The simulation of the wall following behavior of a Khepera robot is used to evaluate the performance of Fuzzy GNP-RL compared with that of GNP-RL. The result shows that Fuzzy GNP-RL is more robust than GNP-RL.

关键词： Fuzzy logic genetic network programming Reinforcement Learning Robustness Wall following behavior

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Hierarchical association rule mining in large and dense databases using genetic network programming

Hierarchical association rule mining in large and dense data...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Gonzales, Eloy Shimada, Kaoru Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9784907764289

In this paper we propose a new hierarchical method to extract association rules from large and dense datasets using genetic network programming (GNP) considering a real world database with a huge number of attributes. It uses three ideas. First, the large database is divided into many small datasets. Second, these small datasets are independently processed by the conventional GNP-based mining method (CGNP) in parallel. This level of processing is called Local Level. Finally, new genetic operations are carried out for small datasets considered as individuals in order to improve the number of rules extracted and their quality as well. This level of processing is called Global Level. The amount of small datasets is also important especially for avoiding the overload and improving the general performance;we find the minimum amount of files needed to extract important association rules. The proposed method shows its effectiveness in simulations using a real world large and dense database.

关键词： data mining association rules genetic network programming parallel processing

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Enhanced Rule Extraction and Classification Mechanism of genetic network programming for Stock Trading Signal Generation 11

Enhanced Rule Extraction and Classification Mechanism of Gen...

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13th Annual genetic and Evolutionary Computation Conference (GECCO)

作者： Mabu, Shingo Hirasawa, Kotaro Waseda Univ Tokyo Japan

ISBN: (纸本)9781450305570

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.

关键词： evolutionary computation rule extraction genetic network programming stock trading technical analysis

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Investigating genetic network programming for Multiple Nest Foraging

Investigating Genetic Network Programming for Multiple Nest ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Foss, Fredrik Stenrud, Truls Haddow, Pauline C. Norw Univ Sci & Tech CRAB Lab Trondheim Norway

ISBN: (纸本)9781728190488

genetic network programming is a relatively unexplored evolutionary algorithm, particularly for more advanced tasks. Foraging is a challenging domain within swarm robotics, since it requires an aptitude for multiple rudimentary behaviours. The work herein thus investigates the application of genetic network programming for multiple nest foraging. Further, a variant of genetic network programming, which incorporates neural network benefits is proposed and evaluated. The results are compared to state-of-the-art foraging algorithms including the generic Neuro-evolution of Augmented Technologies and Novelty Search algorithms and the more application specific Multiple-Place Foraging Algorithm. Results indicate that genetic network programming shows promise.

关键词： genetic network programming NEAT MPFA Foraging Swarm Robots

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A Bidding Strategy using genetic network programming with Adjusting Parameters for Large-scale Continuous Double Auction

A Bidding Strategy using Genetic Network Programming with Ad...

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

作者： Yue, Chuan Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781467317146

Along with the explosive development of electronic commerce, trading goods online becomes much more popular and the trading volume over internet has been increased hugely. Concentrating particularly on continuous double auction (CDA), which is an efficient market mechanism, this paper studied and discussed a genetic network programming (GNP) based bidding strategy with adjusting parameters for autonomous software agents in agent-based large-scale CDAs (GNP-AP). GNP is one of the evolutionary computations, and the individuals with directed graph structures represents the potential bidding strategies. Combined with the heuristic control rules, each individual can collect and judge the auction information, then choose the decision-making transition depending on the judgment results. The parameters of CDAs to select the right decision are adjusted during the evolution in order to get more profits for large-scale CDAs. In the experiments, we studied and discussed the performance of the proposed bidding strategies and compared it with other classic bidding strategies and the previous strategy developed by GNP with rectifying node (GNP-RN) in the large-scale CDA under different settings.

关键词： genetic network programming bidding strategy continuous double auction large-scale CDA

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genetic network programming-Sarsa with Subroutines for Trading Rules on Stock Markets

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第5期15卷 488-494页

作者： Yang, Yang Mabu, Shingo Li, Jianhua Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

The purpose of this paper is to propose a new approach to generate effective subroutines, which are automatically discovered by the GNP-Sarsa programs combining evolution and reinforcement learning. We name it asGNP-Sarsa with subroutines (GNP(sb)-Sarsa) and apply it to the trading rules on stock markets. In the proposal method, GNP(sb)-Sarsa offers an alternative population, where individuals are represented by subroutines. Each main program of GNP(sb)-Sarsa can refer to an individual in the subroutine population, after adding a new kind of node, namely subroutine node, in the graph network structure of GNP-Sarsa. GNP(sb)-Sarsa containing a main program and a subroutine evolves by natural selection and genetic operations, where the gene of GNP(sb)-Sarsa is the pair of the main GNP and its subroutine. That is, the genetic operations on GNP(sb)-Sarsa are constrained by the gene structure on which they can operate. In the simulations, the stock prices of different brands from 2001 to 2004 are used to test the effectiveness of the GNP(sb)-Sarsa. The results show that the proposed approach can provide reasonable opportunities for evolving complex solutions.

关键词： genetic network programming subroutine evolutionary algorithms stock trading model technical index

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Buying and selling stocks of multi brands using genetic network programming with control nodes

Buying and selling stocks of multi brands using genetic netw...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Bao, Zhiguo Mabu, Shigo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9784907764289

A new evolutionary method named "genetic network programming with control nodes, GNPcn" has been proposed. GNPcn represents its solutions as directed graph structures which have some useful features inherently. For example, GNPcn has the implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so very compact graph structures can be made. GNPcn can improve the strategy of buying and selling stocks of multi brands. In this paper, buying and selling stocks of multi brands using GNPcn has been proposed, and its effectiveness is confirmed by simulations.

关键词： buying and selling stocks Candlestick Chart evolutionary computation genetic network programming

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Self-adaptive mechanism in genetic network programming for mining association rules

Self-adaptive mechanism in genetic network programming for m...

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SICE-ICASE International Joint Conference

作者： Taboada, Karla Shimada, Kaoru Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9788995003848

In this paper we propose a method of association rule mining using genetic network programming (GNP) with a self-adaptation mechanism in order to improve the performance of association rule extraction systems. GNP is a kind of evolutionary methods, whose directed graphs are evolved to find a solution as individuals. Self-adaptation behavior in GNP is related to adjust the setting of control parameters such as crossover and mutation rates. It is called self-adaptive because the algorithm controls the setting of these parameters itself - embedding them into an individual's genome and evolving them. The aim is not only to find suitable adjustments but to do this efficiently. Our method can measure the significance of the association via the chi-squared test and obtain a sufficient number of important association rules. Extracted association rules are stored in a pool all together through generations and reflected in three genetic operators as acquired information. Further, our method can contain negation of attributes in association rules and suit association rule mining from dense databases.

关键词： evolutionary computation genetic network programming data mining association rules self-adaptation

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Fuzzy Classification Rule Mining Based on genetic network programming Algorithm

Fuzzy Classification Rule Mining Based on Genetic Network Pr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Taboada, Karla Mabu, Shingo Gonzales, Eloy Shimada, Kaoru Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan

ISBN: (纸本)9781424427932

Association rule-based classification is one of the important data mining techniques applied to many scientific problems. In the last few years, extensive research has been carried out to develop enhanced methods and obtained higher classification accuracies than traditional classifiers. However, the current studies show that the association rule-based classifiers may also suffer some problems inherited from association rule mining such as handling of (1) continuous data and (2) the support/confidence framework. In this paper, a novel fuzzy classification model based on genetic network programming (GNP) that can deal with the above problems has been proposed. GNP is one of the evolutionary optimization algorithms that uses directed graph structures as solutions instead of strings (genetic Algorithms) or trees (genetic programming). Therefore, GNP can deal with more complex problems by using the higher expression ability of graph structures. The performance of our algorithm has been compared with other relevant algorithms and the experimental results show the advantages and effectiveness of the proposed model.

关键词： Association rule mining fuzzy class association rules classification evolutionary computation genetic network programming

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