An introduction to genetic algorithms
An introduction to genetic algorithms
Parallel programming with MPI
Reconstructing distances in physical maps of chromosomes with nonoverlapping probes
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Parallel Monte Carlo Methods for Physical Mapping of Chromosones
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
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Physical map reconstruction in the presence of errorsis a central problem in genetics of high computationalcomplexity. A parallel genetic algorithm fora maximum likelihood estimation-based approach tophysical map reconstruction is presented. The estimationprocedure entails gradient descent search for determiningthe optimal spacings between probes for agiven probe ordering. The optimal probe ordering isdetermined using a genetic algorithm. A two-tier parallelizationstrategy is proposed wherein the gradientdescent search is parallelized at the lower level and thegenetic algorithm is simultaneously parallelized at thehigher level. Implementation and experimental resultson a network of shared-memory symmetric multiprocessors(SMPs) are presented. The genetic algorithm isseen to result in physical maps with fewer contig breakswhen compared to simulated Monte Carlo algorithmssuch as simulated annealing and the large-step Markovchain algorithm.