DNA '00 Revised Papers from the 6th International Workshop on DNA-Based Computers: DNA Computing
DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
Version Space Learning with DNA Molecules
DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
A unified Bayesian framework for evolutionary learning and optimization
Advances in evolutionary computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing
IEEE Transactions on Evolutionary Computation
Molecular programming: evolving genetic programs in a test tube
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary hypernetwork models for aptamer-based cardiovascular disease diagnosis
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Molecular learning of wDNF formulae
DNA'05 Proceedings of the 11th international conference on DNA Computing
DNA hypernetworks for information storage and retrieval
DNA'06 Proceedings of the 12th international conference on DNA Computing
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We use molecular computation to solve pattern classification problems. DNA molecules encode data items and the DNA library represents the empirical probability distribution of data. Molecular bio-lab operations are used to compute conditional probabilities that decide the class label. This probabilistic computational model distinguishes itself from the conventional DNA computing models in that the entire molecular population constitutes the solution to the problem as an ensemble. One important issue in this approach is how to automatically learn the probability distribution of the DNA-based classifier from observed data. Here we develop a molecular evolutionary algorithm inspired by directed evolution, and derive its molecular learning rule from Bayesian decision theory. We investigate through simulation the convergence behaviors of the molecular Bayesian evolutionary algorithm on a concrete problem from statistical pattern classification.