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MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines

Published: 06 July 2013 Publication History

Abstract

In this paper we present MuACOsm - a new method of learning Finite-State Machines (FSM) based on Ant Colony Optimization (ACO) and a graph representation of the search space. The input data is a set of events, a set of actions and the number of states in the target FSM. The goal is to maximize the given fitness function, which is defined on the set of all FSMs with given parameters. The new algorithm is compared with evolutionary algorithms and a genetic programming related approach on the well-known Artificial Ant problem.

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    cover image ACM Conferences
    GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation
    July 2013
    1672 pages
    ISBN:9781450319638
    DOI:10.1145/2463372
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 06 July 2013

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    Author Tags

    1. ant colony optimization
    2. finite-state machine
    3. induction
    4. learning

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    • Research-article

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    GECCO '13
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    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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    GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    Cited By

    View all
    • (2018)GEESEProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205619(999-1006)Online publication date: 2-Jul-2018
    • (2018)Exact finite-state machine identification from scenarios and temporal propertiesInternational Journal on Software Tools for Technology Transfer (STTT)10.1007/s10009-016-0442-120:1(35-55)Online publication date: 1-Feb-2018
    • (2017)Reconstruction of Function Block Logic Using Metaheuristic AlgorithmIEEE Transactions on Industrial Informatics10.1109/TII.2017.271022413:4(1763-1771)Online publication date: Aug-2017
    • (2017)Automatic Inference of Finite-State Plant Models From Traces and Temporal PropertiesIEEE Transactions on Industrial Informatics10.1109/TII.2017.267014613:4(1521-1530)Online publication date: Aug-2017
    • (2017)Epistasis Based ACO for Regression Test Case PrioritizationIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2017.26992281:3(213-223)Online publication date: Jun-2017
    • (2017)CSP-based inference of function block finite-state models from execution traces2017 IEEE 15th International Conference on Industrial Informatics (INDIN)10.1109/INDIN.2017.8104860(714-719)Online publication date: Jul-2017
    • (2016)Small-Moves Based Mutation For Pick-Up And Delivery ProblemProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2931666(1027-1030)Online publication date: 20-Jul-2016
    • (2016)Modified ant colony algorithm for constructing finite state machines from execution scenarios and temporal formulasAutomation and Remote Control10.1134/S000511791603009777:3(473-484)Online publication date: 1-Mar-2016
    • (2016)Plant model inference for closed-loop verification of control systems: Initial explorations2016 IEEE 14th International Conference on Industrial Informatics (INDIN)10.1109/INDIN.2016.7819256(736-739)Online publication date: Jul-2016
    • (2015)Inferring Temporal Properties of Finite-State Machine Models with Genetic ProgrammingProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2768475(1185-1188)Online publication date: 11-Jul-2015
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