Paper Info
Title
MuACOsm: a new mutation-based ant colony optimization algorithm for learning finite-state machines
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.
Year
DOI
Venue
2013
10.1145/2463372.2463440
GECCO
Keywords
Field
DocType
graph representation,ant colony optimization,optimization algorithm,target fsm,fitness function,finite-state machine,genetic programming,evolutionary algorithm,finite-state machines,new method,new algorithm,well-known artificial ant problem,new mutation-based ant colony,finite state machine
Ant colony optimization algorithms,Evolutionary algorithm,Computer science,Genetic programming,Artificial intelligence,Metaheuristic,Mathematical optimization,Parallel metaheuristic,Meta-optimization,Algorithm,Fitness function,Finite-state machine,Machine learning
Conference
Citations 
PageRank 
References 
13
0.84
19
Authors
2
Name
Order
Citations
PageRank
Daniil Chivilikhin1349.41
Vladimir Ulyantsev26012.44