Result: A Novel Graph-Based Estimation of the Distribution Algorithm and Its Extension Using Reinforcement Learning

Title:
A Novel Graph-Based Estimation of the Distribution Algorithm and Its Extension Using Reinforcement Learning
Authors:
XIANNENG LI, SHINGO MABU, HIRASAWA, Kotaro
Source:
IEEE transactions on evolutionary computation. 18(1):98-113
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2014.
Publication Year:
2014
Physical Description:
print, 59 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Intelligence artificielle, Artificial intelligence, Automatique théorique. Systèmes, Control theory. Systems, Robotique, Robotics, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Apprentissage renforcé, Reinforcement learning, Aprendizaje reforzado, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Arbre graphe, Tree(graph), Arbol grafo, Fiabilité, Reliability, Fiabilidad, Robot mobile, Moving robot, Robot móvil, Robotique, Robotics, Robótica, Système multiagent, Multiagent system, Sistema multiagente, Théorie graphe, Graph theory, Teoría grafo, Structure réseau, Network structure, Estructura de redes, Agent control, estimation of distribution algorithm (EDA), genetic network programming (GNP), graph structure, reinforcement learning (RL)
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Graduate School of Information, Production and Systems, Waseda University, Fukuoka 808-0135, Japan
ISSN:
1089-778X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28196124
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28196124
Database:
PASCAL Archive

Further Information

In recent years, numerous studies have drawn the success of estimation of distribution algorithms (EDAs) to avoid the frequent breakage of building blocks of the conventional stochastic genetic operators-based evolutionary algorithms (EAs). In this paper, a novel graph-based EDA called probabilistic model building genetic network programming (PMBGNP) is proposed. Using the distinguished graph (network) structure of a graph-based EA called genetic network programming (GNP), PMBGNP ensures higher expression ability than the conventional EDAs to solve some specific problems. Furthermore, an extended algorithm called reinforced PMBGNP is proposed to combine PMBGNP and reinforcement learning to enhance the performance in terms of fitness values, search speed, and reliability. The proposed algorithms are applied to solve the problems of controlling the agents' behavior. Two problems are selected to demonstrate the effectiveness of the proposed algorithms, including the benchmark one, i.e., the Tileworld system, and a real mobile robot control.