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Learning Finite-State Machines with Ant Colony Optimization
[chapter]
2012
Lecture Notes in Computer Science
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In this paper we present a new method of learning Finite-State Machines (FSM) with the specified value of a given fitness function, which is based on an Ant Colony Optimization algorithm (ACO) and a graph ...
The input data is a set of events, a set of actions and the number of states in the target FSM and the goal is to maximize the given fitness function, which is defined on the set of all FSMs with given ...
This machine allows an ant to eat all food in 189 steps.
Conclusion We have developed an ACO-based local-search heuristic method of learning finite-state machines for a given fitness function. ...
doi:10.1007/978-3-642-32650-9_27
fatcat:cig3j5pwo5a7lggejj7o6wmvs4
Learning Finite-State Machines with Classical and Mutation-Based Ant Colony Optimization: Experimental Evaluation
2013
2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence
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The problem of learning finite-state machines (FSM) is tackled by three Ant Colony Optimization (ACO) algorithms. ...
Here, ants travel between solutions to find the optimal one. In this paper we try to take a step back from the mutationbased ACO to find out if classical ACO algorithms can be used for learning FSMs. ...
Finite-state machines are learned with an evolutionary algorithm in [11] for the Competition for Resources problem. ...
doi:10.1109/brics-cci-cbic.2013.93
fatcat:euyevpcz45citmbr6alskeudlq
MuACOsm
2013
Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - GECCO '13
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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 new algorithm is compared with evolutionary algorithms and a genetic programming related approach on the well-known Artificial Ant problem. ...
CONCLUSION We have developed a new method of learning finite-state machines with the use of ACO. ...
doi:10.1145/2463372.2463440
dblp:conf/gecco/ChivilikhinU13
fatcat:pi426wm2tbevvnvl4bxbhsacsu
Learning Finite-State Machines: Conserving Fitness Function Evaluations by Marking Used Transitions
2013
2013 12th International Conference on Machine Learning and Applications
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This paper is dedicated to the problem of learning finite-state machines (FSMs), which plays a key role in automatabased programming. ...
The proposed method has been incorporated into several traditional and recent FSM learning algorithms based on evolutionary strategies, genetic algorithms and ant colony optimization. ...
LEARNING FINITE-STATE MACHINES WITH MUTATION-BASED METAHEURISTICS In this paper we concentrate on Mealy finite-state machines. ...
doi:10.1109/icmla.2013.111
dblp:conf/icmla/ChivilikhinU13
fatcat:isnch4nsfbcqxkj2wf3bwxeop4
Extended Finite-State Machine Induction Using SAT-Solver
2011
2011 10th International Conference on Machine Learning and Applications and Workshops
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Test-Based Extended Finite-State Machines Induction with Evolutionary Algorithms and Ant Colony Optimization / ...
Alarm clock control system induction · input data: 38 tests for alarm, total length of input sequences 242, total length of answer sequences 195 · comparison with GA and GA+HC · 1000 runs of each algorithm ...
all food in 200 steps 200 200 1 steps n n A f N U n n A f steps 1 . 0 200 200 2 A finite-state machine (FSM) is a sextuple <S, Σ, Δ, δ, λ, s 0 >, where: · Sset of states ...
doi:10.1109/icmla.2011.166
dblp:conf/icmla/UlyantsevT11
fatcat:vr4fbrif7nbmnomouii7h3o6hu
Preface: Swarm Intelligence, Focus on Ant and Particle Swarm Optimization
[chapter]
2007
Swarm Intelligence, Focus on Ant and Particle Swarm Optimization
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The 9 th chapter, "Finite Element Mesh Decomposition Using Evolving Ant Colony Optimization", presents the application of evolving ant colony optimization to the decomposition (partitioning) of finite ...
The proposed chapter also presents the application of predictive neural networks in collaboration with the ant colony optimization method for the decomposition of finite element meshes. ...
doi:10.5772/5121
fatcat:s5xxnkpyejbmtff2d6m3owlpma
Page 2364 of Psychological Abstracts Vol. 90, Issue 7
[page]
2003
Psychological Abstracts
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As an initial at- tempt, our study aims to provide an investigation of the ant colony optimiza- tion approach for coping with tree optimization problems. ...
(Ming Chuan U, Dept of Computer Science & Information Engineering, Taiwan) Ant-Tree: An ant colony optimization approach to the generalized minimum spanning tree problem. ...
A SURVEY ON THE COLLECTIVE BEHAVIOUR OF SWARM ROBOTICS
2020
JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES
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In nature many social animals follow a cooperative behaviour for the common good of their colony. ...
Swarm robotics deals with the defining the rules for the cooperative behaviour and designing, modelling, validating, operating and maintaining the robotics system. ...
Probabilistic finite state machine design: The finite state machine is a design method which takes decision based on the input from the sensors or the memory. ...
doi:10.26782/jmcms.2020.02.00030
fatcat:inwtk2qy6re43kwyycx6llou6m
Scaling ant colony optimization with hierarchical reinforcement learning partitioning
2008
Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08
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The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning and SARSA, with the modified ant colony optimization method, Ant-Q. ...
This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. ...
It replaces the basic reinforcement learning methods used, Q-learning and SARSA, with a basic ant colony optimization algorithm, Ant-Q. ...
doi:10.1145/1389095.1389100
dblp:conf/gecco/DriesP08
fatcat:7zahsvrg4fbajfrav3mwza5ryy
Intelligent Routing Control for MANET Based on Reinforcement Learning
2018
MATEC Web of Conferences
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optimize the node selection strategy through the interaction with the environment and converge to the optimal transmission paths gradually. ...
Aiming at the adaptive routing control with multiple parameters for universal scenes, we propose an intelligent routing control algorithm for MANET based on reinforcement learning, which can constantly ...
With the increase of transmission hops, the optimization objectives of ant colony algorithm and OSPF algorithm increase faster than the proposed RL-INRC algorithm. ...
doi:10.1051/matecconf/201823204002
fatcat:53yk5zin5famploei4ehk2f2za
A Hybrid Feature Subset Selection Approach Based On Svm And Binary Aco. Application To Industrial Diagnosis
2010
Zenodo
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This paper proposes a novel hybrid algorithm for feature selection based on a binary ant colony and SVM. ...
Our algorithm can improve classification accuracy with a small and appropriate feature subset. ...
Huang [20] presents a hybrid ACO-based classifier model that combines ant colony optimization (ACO) and support vector machines (SVM). ...
doi:10.5281/zenodo.1083031
fatcat:jnfp7572xzhnvc3vniqrzcr3re
Bio-inspired Ant Algorithms: A review
2013
International Journal of Modern Education and Computer Science
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Finally a comparison between AAs with well-established machine learning techniques were focused, so that combining with machine learning techniques hybrid, robust, novel algorithms could be produces for ...
Abstract─ Ant Algorithms are techniques for optimizing which were coined in the early 1990"s by M. Dorigo. The techniques were inspired by the foraging behavior of real ants in the nature. ...
Q-learning machine learning technique has been shown.Then compared with without ant algorithm [53] . ...
doi:10.5815/ijmecs.2013.04.04
fatcat:j3z57xy5fffodbwyb6kerrmvjy
Decentralized Multi-tasks Distribution in Heterogeneous Robot Teams by Means of Ant Colony Optimization and Learning Automata
[chapter]
2012
Lecture Notes in Computer Science
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Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. ...
In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony ...
'
-^^
Ant Colony
Optimization
Learning Automata
Ant Colony
Optimization
Learning Automata
Without Noise
Maximum
principle
Strictly random
method
Fig.4(a) ...
doi:10.1007/978-3-642-28942-2_10
fatcat:hl27hpev2zg53dubwy7ujjbtaq
An ant colony optimization algorithm for job shop scheduling problem
[article]
2013
arXiv
pre-print
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The nature has inspired several metaheuristics, outstanding among these is Ant Colony Optimization (ACO), which have proved to be very effective and efficient in problems of high complexity (NP-hard) in ...
This paper describes the implementation of an ACO model algorithm known as Elitist Ant System (EAS), applied to a combinatorial optimization problem called Job Shop Scheduling Problem (JSSP). ...
time required to perform a finite number of tasks in a finite number of machines [13] . ...
arXiv:1309.5110v1
fatcat:6pci5nswybdadezowzl6j6wm3e
Anomaly-Based Intrusion Detection System using Supervised Learning Algorithm Artificial Neural Network and Ant Colony Optimization with Feature Selection
2020
International Journal of Engineering and Advanced Technology
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Artificial neural network (ANN) and Ant Colony Optimization (ACO) with feature selection are the basics of the proposed scheme. ...
The objective of this paper is to detect the intrusion of a system by proposing a Data mining technique which is based on supervised learning algorithm for training dataset. ...
Feature Selection with Ant Colony Optimization: In the proposed model of artificial neural network with Ant colony optimization performed a number of steps to classify KDD CUP 99 Data set into two categories ...
doi:10.35940/ijeat.c5683.029320
fatcat:dezbvwkaebgthi45qrefcudk7m
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