Comparison of sampling sizes for the co-evolution of cooperative agents

Abstract -_The evolution of a heterogeneousteam behavior can he a very demanding task. In order to promote the greatest level of specialization team members should be evolved in separate populations. The greatest complication in the evolution of separate populationsis finding suitable partners for evaluation at trial time. If too few combinationsare tested, the Genetic Algorithm loses its ability to recognize possible solutions and if too many combinations are tested the algorithm becomes too computationally expensive. In previous work a method of punctuated anytime learning was employed to test all combinations of possible partners at periodic generations to reduce the number of evaluations. In further work, it was found that by varying the number of combinations tested, the sample size, the GA could produce an accurate and even less computationally expensive solution. In this paper, we compare different sampling sizes to determine the most effective approach to finding the solution. ...

• Type: Conference
• Year: 2003
• Where: CEC
• Authors: Gary B. Parker, H. Joseph Blumenthal
• Keywords: Abstract -_The Evolution, Artificial Intelligence, CEC 2003, Combinations, Computationally Expensive