MuACOsm - A New Mutation-Based Ant Colony Optimization Algorithm for Learning Finite-State Machines

被引：0
|
作者：
Chivilikhin, Daniil [1 ]
Ulyantsev, Vladimir [1 ]
机构：
[1] St Petersburg Natl Res Univ Informat Technol Mech, St Petersburg, Russia
来源：
GECCO'13: PROCEEDINGS OF THE 2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2013年
关键词：
finite-state machine; learning; induction; ant colony optimization; EVOLUTION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：511 / 518
页数：8
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