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Learning Finite-State Machines with Ant Colony Optimization [chapter]

Daniil Chivilikhin, Vladimir Ulyantsev
2012 Lecture Notes in Computer Science  
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

Daniil Chivilikhin, Vladimir Ulyantsev
2013 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence  
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

Daniil Chivilikhin, Vladimir Ulyantsev
2013 Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - GECCO '13  
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

Daniil Chivilikhin, Vladimir Ulyantsev
2013 2013 12th International Conference on Machine Learning and Applications  
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

V. Ulyantsev, F. Tsarev
2011 2011 10th International Conference on Machine Learning and Applications and Workshops  
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]

Felix T.S., Manoj Kumar
2007 Swarm Intelligence, Focus on Ant and Particle Swarm Optimization  
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  
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

Jeevan J Murthy
2020 JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES  
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

Erik J. Dries, Gilbert L. Peterson
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
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

Fang Dong, Ou Li, Min Tong, Yansong Wang
2018 MATEC Web of Conferences  
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

O. Kadri, M. D. Mouss, L.H. Mouss, F. Merah
2010 Zenodo  
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

Sangita Roy, Sheli Sinha Chaudhuri
2013 International Journal of Modern Education and Computer Science  
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]

Javier de Lope, Darío Maravall, Yadira Quiñonez
2012 Lecture Notes in Computer Science  
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]

Edson Flórez, Wilfredo Gómez, Lola Bautista
2013 arXiv   pre-print
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

Annu Raj, Assistant professor, Vaish College of Engineering, Rohtak., Monika Poriye, Assistant Professor, Department of Computer Science and Applications, Kurukshetra University Kurukshetra
2020 International Journal of Engineering and Advanced Technology  
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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