Multiple alignment, communication cost, and graph matching
SIAM Journal on Applied Mathematics
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
A Graph-based Ant system and its convergence
Future Generation Computer Systems
A polyhedral approach to sequence alignment problems
Discrete Applied Mathematics - Special volume on combinatorial molecular biology
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Alignment graph analysis of embedded discrete-time Markov Chains
CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
Cross-Entropy-Based Replay of Concurrent Programs
FASE '09 Proceedings of the 12th International Conference on Fundamental Approaches to Software Engineering: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009
A cross entropy based algorithm for reliability problems
Journal of Heuristics
Learning to play using low-complexity rule-based policies: illustrations through Ms. Pac-Man
Journal of Artificial Intelligence Research
Sparse and silent coding in neural circuits
Neurocomputing
Using cross-entropy for satisfiability
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Accelerated rare event simulation with Markov chain modelling in wireless communication networks
International Journal of Mobile Network Design and Innovation
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We present a new stochastic method for finding the optimal alignment of DNA sequences. The method works by generating random paths through a graph (the edit graph) according to a Markov chain. Each path is assigned a score, and these scores are used to modify the transition probabilities of the Markov chain. This procedure converges to a fixed path through the graph, corresponding to the optimal (or near-optimal) sequence alignment. The rules with which to update the transition probabilities are based on Rubinstein's Cross-Entropy Method, a new technique for stochastic optimization. This leads to very simple and natural updating formulas. Due to its versatility, mathematical tractability and simplicity, the method has great potential for a large class of combinatorial optimization problems, in particular in biological sciences.