without an objective function. The goodness of each player (candidate solution) is assessed by observing how well it plays against others. An algorithm attempts to use players and their quality of play to obtain better players. The player deemed best of all by the algorithm is the champion. Wolpert and Macready have demonstrated that some coevolutionary algorithms are generally superior to other algorithms in quality of champions obtained. Generating a champion through self-play is of interest in evolutionary computation and game theory. The results are inapplicable to coevolution of biological species, which does not yield champions. See also Evolutionary informatics Inductive bias Occam's razor Simplicity Ugly duckling theorem Notes External links http://www.no-free-lunch.org Radcliffe and Surry, 1995, "Fundamental Limitations on Search Algorithms: Evolutionary Computing in Perspective" (an early published paper on NFL, appearing soon after Schaffer's ICML paper, which in turn was based on Wolpert's preprint; available in various formats) NFL publications by Thomas English NFL publications by Christian Igel and Marc Toussaint NFL and "free lunch" publications by Darrell Whitley Old list of publications by David Wolpert, William Macready, and Mario Koeppen on optimization and search Mathematical optimization Theorems in computational complexity theory