Sentence examples for improve the crossover from inspiring English sources

With an eye to improve the performance of classical DE, in this paper, a DE algorithm combined with truncated Lévy flight random walks and a population diversity measure (DEL) to improve the crossover and mutation operations is designed to help avoiding premature convergence effectively.

This would improve the efficiency of crossover interference, since complete SC localization to chromosome axes is required for the execution of crossover interference (Hayashi et al. 2010; Hillers and Villeneuve 2003; Sym and Roeder 1994).

Based on the information of a single loop caused by closing a normally open switch, we improve the algorithm on crossover and mutation operations of original Genetic Algorithms.

Thirdly, to improve the convergence, a crossover operator based on the uniform design method is constructed to enhance the search capacity and the CDAS is used to sort solutions of each sub-population to guide the search process to converge the Pareto optimal solutions.

By using the CEP mechanism, the search algorithm not only can retain elitists to improve the efficiency of crossover, but also can get better energy value and RMSD.

Consequently, to maintain the population diversity and improve the convergence speed, crossover should be gradually utilized with larger values along with the generations of evolution increased to preserve well genes in so far as possible and promote the convergence performance.

Also the hill climbing is used in order to improve the chromosomes obtained through crossover and mutation.

According to Figure 4, the hill climbing operator has the main role to improve the chromosomes obtained through crossover and mutation, and it works highly efficient.

In order to improve the performance of quantum interference crossover, a bi-direction quantum crossover is proposed based on the quantum jump theory.

In order to improve the performance of GA multipoint crossover with random splice points has been considered, while mutation probability and population are assumed as 0.1 and N, respectively.

Future work should focus on evaluating potential building blocks, so that crossover can improve the efficiency of the search.