Tag: active information
For Beleaguered Computer Simulations of Evolution, Can Co-Evolution Save the Day?
A familiar illustration of co-evolution is the relationship between honey bees and flowers.
William Dembski and Robert Marks Publish (Another) Peer-Reviewed Scientific Paper Supporting No Free Lunch Theorems
A peer-reviewed scientific paper published in 2010 by William Dembski and Robert Marks of the Evolutionary Informatics Lab supports no free lunch theorems. Published in Journal of Advanced Computational Intelligence and Intelligent Informatics and titled “The Search for a Search: Measuring the Information Cost of Higher Level Search,” the paper’s abstract states that unless one has information about a target, search engines often fail: “Needle-in-the-haystack problems look for small targets in large spaces. In such cases, blind search stands no hope of success.” Their principle of Conservation of Information holds that “any search technique will work, on average, as well as blind search.” However, in such a case “[s]uccess requires an assisted search. But whence the assistance required for a Read More ›
Winston Ewert, William Dembski, and Robert Marks Publish Mainstream Scientific Paper Exposing Flaws in Avida Evolution Simulation
Darwinian evolution has no prior knowledge about the search target, but Avida’s programmers have intelligently designed Avida by smuggling in “active information.”
William Dembski and Robert Marks Publish Mainstream Scientific Paper on Conservation of Information
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 Read More ›