Name
Title | ||
|---|---|---|
A variable size mechanism of distributed graph programs and its performance evaluation in agent control problems |
Abstract | ||
|---|---|---|
Genetic Algorithm (GA) and Genetic Programming (GP) are typical evolutionary algorithms using string and tree structures, respectively, and there have been many studies on the extension of GA and GP. How to represent solutions, e.g., strings, trees, graphs, etc., is one of the important research topics and Genetic Network Programming (GNP) has been proposed as one of the graph-based evolutionary algorithms. GNP represents its solutions using directed graph structures and has been applied to many applications. However, when GNP is applied to complex real world systems, large size of the programs is needed to represent various kinds of control rules. In this case, the efficiency of evolution and the performance of the systems may decrease due to its huge structures. Therefore, we have been studied distributed GNP based on the idea of divide and conquer, where the programs are divided into several subprograms and they cooperatively control whole tasks. However, because the previous work divided a program into some subprograms with the same size, it cannot adjust the sizes of the subprograms depending on the problems. Therefore, in this paper, an efficient evolutionary algorithm of variable size distributed GNP is proposed and its performance is evaluated by the tileworld problem that is one of the benchmark problems of multiagent systems in dynamic environments. The simulation results show that the proposed method obtains better fitness and generalization abilities than the method without variable size mechanism. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.eswa.2013.08.063 | Expert Syst. Appl. |
Keywords | Field | DocType |
genetic programming,genetic network programming,graph program,genetic algorithm,variable size mechanism,agent control problem,variable size,large size,performance evaluation,typical evolutionary algorithm,graph-based evolutionary algorithm,efficient evolutionary algorithm,directed graph,evolutionary computation,reinforcement learning | Evolutionary algorithm,Computer science,Directed graph,Evolutionary computation,Genetic programming,Theoretical computer science,Tree structure,Artificial intelligence,Divide and conquer algorithms,Machine learning,Genetic algorithm,Reinforcement learning | Journal |
Volume | Issue | ISSN |
41 | 4 | 0957-4174 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shingo Mabu | 1 | 493 | 77.00 |
| Kotaro Hirasawa | 2 | 704 | 113.11 |
| Masanao Obayashi | 3 | 198 | 26.10 |
| Takashi Kuremoto | 4 | 196 | 27.73 |