This paper presents an approach for controlling gene networks based on a Markov chain model, where the state of a gene network is represented as a probability distribution, while ...
Physical map reconstruction in the presence of errors is a central problem in genetics of high computational complexity. A parallel genetic algorithm for a maximum likelihood esti...
Suchendra M. Bhandarkar, Jinling Huang, Jonathan A...
The links between genetic algorithms and population-based Markov Chain Monte Carlo (MCMC) methods are explored. Genetic algorithms (GAs) are well-known for their capability to opt...
A new approach for overcoming broken ergodicity in Markov Chain Monte Carlo (MCMC) simulations of complex systems is described. The problem of broken ergodicity is often present i...
Abstract--The increasing availability of multi-core and multiprocessor architectures provides new opportunities for improving the performance of many computer simulations. Markov C...
Jonathan M. R. Byrd, Stephen A. Jarvis, Abhir H. B...