PROBABILITY, OPTIMIZATION THEORY, AND EVOLUTION 1

Perhaps it is not surprising that mathematics has always been popular among anti-evolutionists. Math is unique in its ability to bamboozle a lay audience, making it well-suited to their purposes. William Dembski, of Baylor University, represents the cutting edge in anti-Darwinian mathematics. His bailiwick is probability and information theory, which he fashions into a formidable, but ultimately unsuccessful , weapon. For years the Holy Grail of optimization theory was the production of an algorithm that would outperform blind search independent of the particular problem to be solved. The No Free Lunch (NFL) of Demb-ski's title refers to a collection of theorems establishing the nonexistence of such an algorithm (Wolpert and Macready, 1996). Specifically, the average performance of any algorithm over the class of all optimization problems is no better than blind search. It follows that an algorithm is assured of success only when information about the problem is in some way built into it. Dembski presumes to use NFL as the foundation of an argument against the explanatory sufficiency of natural selection. In the first three chapters of the book he argues that complex specified information (CSI) is a reliable indicator of design. For Dembski this is a technical term in probability theory. Mathematically speaking, information content is something possessed by an event in a probability space. " Complex " then indicates an event of low probability, while " specified " notes the event's conformity to some independently describable pattern. He then argues that biological systems are replete with CSI and that NFL precludes selection's ability to create such information without preexisting CSI to act upon.