Name
Abstract | ||
|---|---|---|
In this paper we introduce two macro-level operators to enhance the use of population-based evolutionary computing techniques in multiagent environments: speciation and symbiogenesis. We describe their use in conjunction with the genetic algorithm to evolve Pittsburgh-style classifier systems, where each classifier system represents an agent in a cooperative multi-agent system. The reasons for implementing these kinds of operators are discussed and we then examine their performance in developing a controller for the gait of a wall-climbing quadrupedal robot, where each leg of the quadruped is controlled by a classifier system. We find that the use of such operators can give improved performance over static population/agent configurations. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1007/3-540-61723-X_965 | PPSN |
Keywords | Field | DocType |
evolutionary computing,multi-agent environments,multi agent system,genetic algorithm | Population,Control theory,Computer science,Evolutionary computation,Artificial intelligence,Operator (computer programming),Classifier (linguistics),Evolutionary programming,Robot,Machine learning,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
3-540-61723-X | 5 | 0.94 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lawrence Bull | 1 | 5 | 0.94 |
| T C Fogarty | 2 | 1147 | 152.53 |