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Test-based extended finite-state machines induction with evolutionary algorithms and ant colony optimization

Published: 07 July 2012 Publication History

Abstract

In this paper we consider the problem of extended finite-state machines induction. The input data for this problem is a set of tests. Each test consists of two sequences - an input sequence and a corresponding output sequence. We present a new method of Extended Finite-State Machines (EFSM) induction based on an Ant Colony Optimization algorithm (ACO) and a new meaningful test-based crossover operator for EFSMs. New algorithms are compared with a genetic algorithm (GA) using a traditional crossover, a (1+1) evolutionary strategy and a random mutation hill climber. This comparison shows that the use of test-based crossover greatly improves performance of GA. GA on average also significantly outperforms the hill climber and evolutionary strategy. ACO outperforms GA, and the difference between average performance of ACO and GA hybridized with hill climber is insignificant.

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Cited By

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  • (2020)A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet InspectionArchives of Computational Methods in Engineering10.1007/s11831-020-09419-z28:3(1371-1396)Online publication date: 15-Apr-2020
  • (2013)MuACOsmProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463440(511-518)Online publication date: 6-Jul-2013
  • (2013)Learning Finite-State MachinesProceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2013.111(90-95)Online publication date: 4-Dec-2013
  • Show More Cited By

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cover image ACM Conferences
GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
July 2012
1586 pages
ISBN:9781450311786
DOI:10.1145/2330784
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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Published: 07 July 2012

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Author Tags

  1. ant colony optimization
  2. crossover
  3. extended finite-state machine induction
  4. genetic algorithm

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GECCO '12
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GECCO '12: Genetic and Evolutionary Computation Conference
July 7 - 11, 2012
Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2020)A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet InspectionArchives of Computational Methods in Engineering10.1007/s11831-020-09419-z28:3(1371-1396)Online publication date: 15-Apr-2020
  • (2013)MuACOsmProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463440(511-518)Online publication date: 6-Jul-2013
  • (2013)Learning Finite-State MachinesProceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2013.111(90-95)Online publication date: 4-Dec-2013
  • (2013)Learning Finite-State Machines with Classical and Mutation-Based Ant Colony OptimizationProceedings of the 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence10.1109/BRICS-CCI-CBIC.2013.93(528-533)Online publication date: 8-Sep-2013

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