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Understanding EA Dynamics via Population Fitness Distributions
Elena Popovici and Kenneth De Jong
Department of Computer Science
George Mason University
Fairfax, VA 22030
Abstract. This paper introduces a new tool to be used in conjunction with existing ones for a more comprehensive understanding of the behavior of evolutionary algorithms. Several research groups including [1,3,4] have shown how deeper insights into EA behavior can be obtained by focusing on the changes to the entire population fitness distribution rather than just "best-so-far" curves. But characterizing how repeated applications of selection and reproduction modify this distribution over time proved to be very difficult to achieve analytically and was done successfully for only a few very specialized EAs and/or very simple fitness landscapes.
LNCS 2724, p. 1604 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003