Stuart Kauffman: evolutionary program information (Introduction)

by David Turell , Friday, February 06, 2015, 20:39 (3577 days ago) @ David Turell

An article on the subject of the need for information:-"According to conservation of information theorems, performance of an arbitrarily chosen search, on average, does no better than blind search. Domain expertise and prior knowledge about search space structure or target location is therefore essential in crafting the search algorithm. The effectiveness of a given algorithm can be measured by the active information introduced to the search. We illustrate this by identifying sources of active information in Avida, a software program designed to search for logic functions using nand gates. Avida uses stair step active information by rewarding logic functions using a smaller number of nands to construct functions requiring more. Removing stair steps deteriorates Avida's performance while removing deleterious instructions improves it. Some search algorithms use prior knowledge better than others. For the Avida digital organism, a simple evolutionary strategy generates the Avida target in far fewer instructions using only the prior knowledge available to Avida."-http://evoinfo.org/publications/evolutionary-synthesis-of-nand-logic-avida/-"III. CONCLUSIONS
A. Active Information
The Avida program uses numerous sources of active information to guide its performance to successful discovery of the EQU logic function. The sources include the following.
• Stair step active information. In the initial description of Avida, the authors write [16] “Some readers might suggest that we stacked the deck by studying the evolution of a complex feature that could be built on simpler functions that were
also useful.” This, indeed, is what the writers of Avida software do when using stair step active information. The importance of stair step active information is evident from the inability to generate a single EQU in Avida without using it
[16].
• Active information from Avida's initialization. The initialization in Avida recognizes the essential role of the nop-C instruction in finding the EQU. Initializing using all nonessential nop-A or nop-B instructions results in
the a decrease in NAIPI in Avida.
• Mutation, fitness, and choosing the fittest of a number of mutated offspring [5] are additional sources of active information in Avida we have not explored in this paper.