Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The evolution of heterogeneous team behaviour can be 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 populations is finding suitable partners for evolution at trial time. If too few combinations are 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 works, 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. ,  compare different sampling sizes to determine the most effective approach to finding the solution.  use a box pushing task to compare these different sampling sizes.
['Gary B. Parker', 'H.J. Blumenthal']
I am writing a research paper on a idea Comparison of sampling sizes for the co-evolution of cooperative agents
[{"from": "human", "value": "I am writing a research paper on a idea Comparison of sampling sizes for the co-evolution of cooperative agents"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The evolution of heterogeneous team behaviour can be 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 populations is finding suitable partners for evolution at trial time. If too few combinations are 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 works, 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. ,  compare different sampling sizes to determine the most effective approach to finding the solution.  use a box pushing task to compare these different sampling sizes."}]