Algorithms for clustering data
Algorithms for clustering data
Vector quantization and signal compression
Vector quantization and signal compression
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
MPI: The Complete Reference
Journal of Global Optimization
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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In the paper the problem of using an evolutionary algorithm to partition a dataset into a known number of clusters is considered. A novel approach, based on data decomposition, for parallel computing of the fitness function is proposed. Both the learning set and the population of the evolutionary algorithm are distributed among processors. Processors form a pipeline using the ring topology. In a single step each processor computes the local fitness of its current subpopulation while sending the previous subpopulation to the successor and receiving next subpopulation from the predecessor. Thus it is possible to overlap communication and computation using non-blocking MPI routines. Our approach to parallel fitness computation was applied to differential evolution algorithm. The results of initial experiments show, that for large datasets the algorithm is capable of achieving very good scalability.