Coevolution can be used to adaptively choose the tests used for evaluating candidate solutions. A long-standing question is how this dynamic setup may be organized to yield reliable search methods. Reliability can only be considered in connection with a particular solution concept specifying what constitutes a solution. Recently, monotonic coevolution algorithms have been proposed for several solution concepts. Here, we introduce a new algorithm that guarantees monotonicity for the solution concept of maximizing the expected utility of a candidate solution. The method, called MaxSolve, is compared to the IPCA algorithm and found to perform more efficiently for a range of parameter values on an abstract test problem.
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