Title :
A Novel Graph-Based Estimation of the Distribution Algorithm and its Extension Using Reinforcement Learning
Author :
Xianneng Li ; Mabu, Shingo ; Hirasawa, K.
Author_Institution :
Grad. Sch. of Inf., Production & Syst., Waseda Univ., Fukuoka, Japan
Abstract :
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.
Keywords :
genetic algorithms; graph theory; learning (artificial intelligence); multi-agent systems; network theory (graphs); probability; EA; EDA extension; Tileworld system; agent behavior; conventional stochastic genetic operators; distinguished graph structure; estimation-of-the-distribution algorithm; evolutionary algorithms; graph-based EDA; mobile robot control; probabilistic model building genetic network programming; reinforced PMBGNP algorithm; reinforcement learning; Boltzmann distribution; Delay effects; Economic indicators; Genetics; Probabilistic logic; Sociology; Agent control; estimation of distribution algorithm (EDA); genetic network programming (GNP); graph structure; reinforcement learning (RL);
Journal_Title :
Evolutionary Computation, IEEE Transactions on
DOI :
10.1109/TEVC.2013.2238240