One of the primary obstacles to Evolutionary Algorithms (EAs) fulfilling their promise as easy to use general-purpose problem solvers is the difficulty of correctly configuring them for specific problems such as to obtain satisfactory performance. This paper introduces the concept of democratic, semi-autonomous parent selection by encoding and evolving population rating operators as in Genetic Programming and shows the potential of extending self-adaptation by pairing mates using an adaptation of the Stable Roommates problem. Replacing the typical general parent selection algorithm with autonomously evolved individual selection parameters has the prospective to bring EAs a step closer to their promise as easy to use general-purpose problem solvers.
Eads, Joshua M., "Improving the Usability of Evolutionary Algorithms: Self-Adaptive Semi-Autonomous Democratic Parent Selection" (2007). Opportunities for Undergraduate Research Experience Program (OURE). 190.