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检索条件"主题词=Genetic Network Programming"

共 139 条记录，以下是71-80 订阅

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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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Enhancing Interpretability in Machine Learning: A Focus on genetic network programming, Its Variants, and Applications 10th

Enhancing Interpretability in Machine Learning: A Focus on G...

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10th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Moravvej, Seyed Vahid Joloudari, Javad Hassannataj Alinejad-Rokny, Hamid Gorriz, Juan M. Fasa Univ Fac Engn Dept Comp Engn Fasa Iran Deakin Univ Intelligent Syst Res & Innovat IISRI Geelong Vic Australia Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Tech & Vocat Univ TVU Dept Comp Engn Tehran Iran UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab Sydney NSW 2052 Australia Univ Granada Dept Signal Theory Networking & Commun Granada Spain

ISBN: (纸本)9783031611391;9783031611407

In current machine learning research, deep learning methodologies have become the prevalent approach across various domains, including decision-making processes. However, the interpretability of solutions generated by these algorithms remains a significant challenge, as these models do not inherently prioritize explainability. This lack of interpretability hampers the analysis of decision-making rationales. One potential remedy to this issue is the employment of genetic network programming (GNP), a method within the evolutionary computing paradigm, known for its ability to generate more interpretable solutions. This study provides a concise overview of GNP, exploring its modifications and applications to demonstrate its utility in addressing the interpretability challenge in machine learning algorithms.

关键词： genetic programming genetic network programming Evolutionary Computing

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Evaluation on the Robustness of genetic network programming with Reinforcement Learning

Evaluation on the Robustness of Genetic Network Programming ...

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IEEE International Conference on Systems, Man and Cybernetics

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

ISBN: (纸本)9781424465880

genetic network programming (GNP) has been proposed as one of the evolutionary algorithms and extended with reinforcement learning (GNP-RL). The combination of evolution and learning can efficiently evolve programs and the fitness improvement has been confirmed in the simulations of tileworld problems, elevator group supervisory control systems, stock trading models and wall following behavior of Kbepera robot. However, its robustness in testing environments has not been analyzed in detail yet. In this paper, the learning mechanism in the testing environment is introduced and it is confirmed that GNPRL can show the robustness using a robot simulator WEBOTS, especially when unexperienced sensor troubles suddenly occur. The simulation results show that GNP-RL works well in the testing even if wrong sensor information is given because GNPRL has a function to change programs using alternative actions automatically. In addition, the analysis on the effects of the parameters of GNP-RL is carried out in both training and testing simulations.

关键词： evolutionary computation genetic network programming reinforcement learning robustness Kbepera robot

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Enhancement of trading rules on stock markets using genetic network programming with Sarsa Learning

Enhancement of trading rules on stock markets using Genetic ...

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

作者： Chen, Yan Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka Japan

ISBN: (纸本)9784907764289

In this paper, the enhancement of trading rules on stock markets using genetic network programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa learning as the basic algorithm while importance Indices and Candlestick Charts are introduced for efficient stock trading decision-making. Importance indices have been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we develop a new method that can learn appropriate function describing the relation between the value of each technical index and the output of the importance index (IMX). This is an important point that devotes to the enhancement of the proposed GNP-Sarsa algorithm. Third, in order to create more efficient judgment functions to judge the current stock price appropriately, we develop a new way of classifying the candlestick chart body type. To confirm the effectiveness of the proposed method, we also compare the simulation results using GNP-Sarsa with other methods like traditional GNP and Buy&Hold method.

关键词： genetic network programming reinforcement learning Sarsa stock trading model technical index candlestick chart

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Multi-Car Elevator System using genetic network programming

Multi-Car Elevator System using Genetic Network Programming

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Annual Conference of the SICE

作者： Yu, Lu Zhou, Jin Mabu, Shingo Shimada, Kaoru Hirasawa, Kotaro Markon, Sandor Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Hibikino 2 Fukuoka 8030235 Japan Fujitec Co Ltd Hikone Shiga 5228588 Japan

ISBN: (纸本)9784907764302

Elevator group control systems are the control systems that systematically manage elevators in order to transport passengers efficiently. With the increasing need for-high-performance transportation systems in buildings, multicar elevators where two cars operate separately and independently in an elevator shaft are attracting attention as the next novel elevator system. genetic network programming(GNP) can introduce various priori knowledge of the elevator systems in its node functions easily and execute an efficient rule-based group control that is optimized evolutionary. This paper discusses the development of controllers for Multi-Car Elevator System(MCES) using GNP. The effects for MCES are examined, and we compare the advantages and performances between MCES and Double-Deck Elevator System(DDES).

关键词： multi-car elevator system genetic network programming destination floor guidance system evolutionary computation

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Autonomous acquisition of cooperative behavior based on a theory of mind using parallel genetic network programming

Autonomous acquisition of cooperative behavior based on a th...

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16th International Symposium on Artificial Life and Robotics (AROB 16th '11)

作者： Minoya, Kenichi Arita, Takaya Omori, Takashi Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan Tamagawa Univ Coll Engn Machida Tokyo 1948610 Japan

ISBN: (纸本)9784990288051

Understanding of others as having intentional states such as beliefs and desires is called Theory of Mind (ToM). To clarify the mechanism of the autonomous acquisition of cooperative behavior based on the ToM, we constructed a functional model of the brain based on the Functional Parts Combination (FPC) model. This model consists of a set of functional parts and activation signals specifying selective activated patterns, and activated modules can be executed in parallel based on the flow of control tokens. The module network and activation signals can be acquired by the evolutionary computation techniques used in the genetic network programming and genetic Algorithm, respectively. We use a hunter task as a task to be solved by the agents, and encode inherent activation signals into the genome as a first step. The result of computer simulation shows an emergence of the pattern of the functional parts for processing ToM through evolution characterized by punctuated equilibrium.

关键词： Functional Model of the Brain Theory of Mind Cooperation Evolution genetic network programming

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Trading Rules on Stock Markets Using genetic network programming with Sarsa Learning 07

Trading Rules on Stock Markets Using Genetic Network Program...

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Annual Conference of genetic and Evolutionary Computation Conference

作者： Chen, Yan Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781595936974

In this paper, the genetic network programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the buying and selling timing of stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.

关键词： genetic network programming Reinforcement Learning Sarsa Stock Trading Model Technical Index Candlestick Chart

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Variable Size genetic network programming with Binomial Distribution

Variable Size Genetic Network Programming with Binomial Dist...

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IEEE Congress on Evolutionary Computation (CEC)

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

ISBN: (纸本)9781424478354

This paper proposes a different type of genetic network programming (GNP) - Variable Size genetic network programming (GNPvs) with Binomial Distribution. In contrast to the individuals with fixed size in Standard GNP, GNPvs will change the size of the individuals and obtain the optimal size of them during evolution. The proposed method defines a new type of crossover to implement the new feature of GNP. The new crossover will select the number of nodes to move from each parent GNP to another parent GNP. The probability of selecting the number of nodes to move satisfies the binomial probability distribution. The proposed method can keep the effectiveness of crossover and improve the performance of GNP. In order to verify the performance of the proposed method, a well-known benchmark problem - Tile-world is used in the simulations. The simulation results show the effectiveness of the proposed method.

关键词： genetic network programming Variable size Crossover Binomial probability distribution Tile-world

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Association Rule Mining for Continuous Attributes using genetic network programming 07

Association Rule Mining for Continuous Attributes using Gene...

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Annual Conference of genetic and Evolutionary Computation Conference

作者： Taboada, Karla Shimada, Kaoru Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781595936974

Most association rule mining algorithms make use of discretization algorithms for handling continuous attributes. However, by means of methods of discretization, it is difficult to get highest attribute interdependency and at the same time to get lowest number of intervals. We propose a method using a new graph-based evolutionary algorithm named "genetic network programming (GNP)" that can deal with continues values directly, that is, without using any discretization method as a preprocessing step. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as solutions and is composed of three kinds of nodes: start node, judgment node and processing node. Once GNP is booted up, firstly the execution starts from the start node, secondly the next node to be executed is determined according to the judgment and connection from the current activated node. The features of GNP are described as follows. First, it is possible to reuse nodes; because of this, the structure is compact. Second, GNP can find solutions of problems without bloat, which can be sometimes found in genetic programming (GP), because of the fixed number of nodes in GNP. Third, nodes that are not used at the current program executions will be used for future evolution. Fourth, GNP is able to cope with partially observable Markov processes. In this paper, we propose a method that can deal with continuous attributes, where attributes in databases correspond to judgment nodes in GNP and each continuous attribute is checked whether its value is greater than a threshold value and the association rules are represented as the connections of the judgment nodes. Threshold ai is firstly determined by calculating the mean µi and standard deviation si of all attribute values of Ai. Then, initial threshold ai is selected randomly between the interval [µi - aisi, µi + aisi] where ai is a parameter to determine the range of the interval. Once the threshold ai is selected for all attrib

关键词： continuous attributes genetic network programming association rules mining

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A Multitasks Learning Approach to Autonomous Agent based on genetic network programming

A Multitasks Learning Approach to Autonomous Agent based on ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / International Joint Conference on Neural networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE-CEC) / IEEE World Congress on Computational Intelligence (IEEE-WCCI)

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

ISBN: (纸本)9781467315098

The standard methodology in machine learning is to learn one problem at a time. But, many real-world problems are complex and have multitasks, and it is a bit hard to learn them well by one machine learning approach. So, the simultaneous learning of several tasks has been considered, that is, so-called multitask learning. This paper describes a new approach to the autonomous agent problem using the multitask learning scheme based on genetic network programming (GNP), called ML-GNP, where each GNP is used to learn one corresponding task. ML-GNP has some charateristics, such as distribution, interaction and autonomy, which are helpful for learning multitask problems. The experimental results illustrate that ML-GNP can give much better performance than learning all the tasks of the problem by one GNP algorithm.

关键词： Multitasks Learning Approach genetic network programming network programming Intelligent agents PARALLEL PROCESSING (COMPUTERS) economic indicators Religious Missions Learning Autonomy

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