Deconstructing the digit recognition problem
ML92 Proceedings of the ninth international workshop on Machine learning
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Digital system design using field programmable gate arrays
Digital system design using field programmable gate arrays
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Conservation of information in search: measuring the cost of success
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Remarks on a recent paper on the "no free lunch" theorems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Bernoulli's principle of insufficient reason and conservation of information in computer search
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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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.