Is there a “magic bullet” mechanism by which blind and unguided search engines can find rare, isolated targets? This question may seem esoteric, but it’s the precise problem facing Darwinian evolution. In a new scientific paper published in Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, Discovery Institute senior fellow William Dembski and Robert J. Marks explain why Bernoulli’s Principle of Insufficient Reason dictates that without prior knowledge about the search target or the search space, no search algorithm will ever increase the probability of finding the target. Any search that increases the probability of finding the target smuggles in “active information” about the target’s location or the search space. In other words, when it comes to finding rare targets in search space, there’s no such thing as a “free lunch.” The implications for Darwinism are potent: the “limited number of endpoints on which evolution converges constitute intrinsic targets,” and thus “in biology, as in computing, there is no free lunch.” According to this paper, the Darwinian mechanism is thus not the efficient search engine many claim it is. The abstract reads:

Conservation of information (COI) popularized by the no free lunch theorem is a great leveler of search algorithms, showing that on average no search outperforms any other. Yet in practice some searches appear to outperform others. In consequence, some have questioned the significance of COI to the performance of search algorithms. An underlying foundation of COI is Bernoulli’s Principle of Insufficient Reason (PrOIR) which imposes of a uniform distribution on a search space in the absence of all prior knowledge about the search target or the search space structure. The assumption is conserved under mapping. If the probability of finding a target in a search space is p, then the problem of finding the target in any subset of the search space is p. More generally, all some-to-many mappings of a uniform search space result in a new search space where the chance of doing better than p is 50-50. Consequently the chance of doing worse is 50-50. This result can be viewed as a confirming property of COI. To properly assess the significance of the COI for search, one must completely identify the precise sources of information that affect search performance. This discussion leads to resolution of the seeming conflict between COI and the observation that some search algorithms perform well on a large class of problems.

William A. Dembski, and Robert J. Marks II, “Bernoulli’s Principle of Insufficient Reason and Conservation of Information in Computer Search,” Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA, 2647-2652 (October 2009).