Combining Exploration and Reliability in Coevolution
Coevolution can in principle circumvent the difficult problem of designing a fitness function; this may remove harmful biases, and thereby improve search performance. Recently, based on Evolutionary Multi-Objective Optimization, the feasibility of accurate evaluation in coevolution has been demonstrated theoretically. A current challenge in coevolution is how this theoretical promise may be translated into practical algorithms. We investigate a setup consisting of multiple subpopulations, each with a varying degree of exploration. It is shown that by allowing these subpopulations to interact, the desirable aims of exploration and reliability can be combined.