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Biologic Highlights New Peer-Reviewed Paper In BIO-Complexity On Evolutionary Algorithms

At Biologic Institute's website Ann Gauger has posted a piece about the new peer-reviewed article in BIO-Complexity :

In the recent past, several papers have been published that claim to demonstrate that biological evolution can readily produce new genetic information, using as their evidence the ability of various evolutionary algorithms to find a specific target. This is a rather large claim.

It has thus fallen to others in the scientific or engineering community to evaluate these published claims. How well do these algorithms model biology? How exactly was the work done? Do the results make sense? Are there unexamined variables that might affect the interpretation of results? Are there hidden sources of bias? Are the conclusions justified or do they go beyond the scope of what has been shown?

Read the rest here.

The full journal article can be accessed freely through BIO-Complexity's website here.

A Vivisection of the ev Computer Organism: Identifying Sources of Active Information
George MontaƱez, Winston Ewert, William Dembski, Robert Marks
Abstract
ev is an evolutionary search algorithm proposed to simulate biological evolution. As such, researchers have claimed that it demonstrates that a blind, unguided search is able to generate new information. However, analysis shows that any non-trivial computer search needs to exploit one or more sources of knowledge to make the search successful. Search algorithms mine active information from these resources, with some search algorithms performing better than others. We illustrate these principles in the analysis of ev. The sources of knowledge in ev include a Hamming oracle and a perceptron structure that predisposes the search towards its target. The original ev uses these resources in an evolutionary algorithm. Although the evolutionary algorithm finds the target, we demonstrate a simple stochastic hill climbing algorithm uses the resources more efficiently.