Sentence examples for crossover strategy from inspiring English sources

She tried one international crossover strategy: putting synthesizers and a common-denominator 4/4 club beat under one medley of hits.

Accordingly, a novel crossover strategy is introduced.

In AGA, an adaptive crossover strategy is used to improve the search performance of the algorithm.

The learning process of DMELA in each group involves seven major operators: initialization, SOA, DMSM, fitness assignment, reproduction strategy, crossover strategy, and mutation strategy.

In spite of many new strings the crossover strategy produced, new information to every group at the site of an individual is still not provided by these strings.

In the hormone receptor positive group, there was a one-third improvement in disease-free survival and a near survival advantage (P = 0.08) for the crossover strategy.

Performance of the proposed GWO algorithm has been compared with comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE) and other similar meta-heuristic optimization techniques available in literature for similar test system.

In this study we are served well by the most basic mutation and crossover strategies, with conservative values of C and F. Investigation of the effects of varying C, F, and mutation and crossover strategies is beyond the scope of this paper.

Three other trials have employed crossover strategies; the Intergroup Exemestane Study IESS) and the Austrian Breast & Colorectal Cancer Study Group (ABCSG) Trial 8/German ARNO 95 trial (anastrozole) compared a crossover to an aromatase inhibitor versus continued tamoxifen in women who had completed 2 3 years of tamoxifen [ 5, 6].

These experiments show that using a hybrid BP-GENN training algorithm, alternative crossover strategies, and incorporating domain knowledge into the search for genes related to disease can aid the variable selection and model fitting process of ATHENA.

We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait.