Agent-based optimization of advisory strategy parameters

In this paper, an application of Evolutionary Multiagent Systems (EMAS) and its memetic version to the optimization of advisory strategy (in this case, Sudoku advisory strategy) is considered. The problem is tackled using an EMAS, which has already proven as a versatile optimization technique. Results obtained using EMAS and Parallel Evolutionary Algorithm (PEA) are compared. After giving an insight to the possibilities of decision support in Sudoku solving, an exemplary strategy is defined. Then EMAS and its memetic versions are discussed, and experimental results concerning comparison of EMAS and PEA presented.