Evolving prediction weights using evolution strategy

The evolution strategy is one of the strongest evolutionary algorithms for optimizing real-value vectors. In this paper, we study how to use it for the evolution of prediction weights in XCSF in order to make the computed prediction more accurate. Our version of XCSF shows to be able to evolve more accurate linear approximations of functions. It is more efficient than the original XCSF and slightly better than XCSF with recursive least squares, in spite of its simple structure and its low complexity. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning—Concept learning, Parameter learning General Terms Algorithms, Performance Keywords XCSF, function approximation, evolution strategy

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Trung Hau Tran, Cédric Sanza, Yves Duthen
• Keywords: Accurate Linear Approximations, Evolution Strategy, GECCO 2008, Optimization, Strongest Evolutionary Algorithms