The configuration of the hyperparameters in convolutional neural networks (CNN) is crucial for determining their performance. However, traditional methods hyperparameter configuration, such as grid searches and random searches, are time consuming labor intensive. optimization CNN a complex problem involving multiple local optima that poses challenge particle swarm (PSO) algorithms, which prone ...

In bacterial, yeast, and human cells, stress-induced mutation mechanisms are induced in growth-limiting environments and produce non-adaptive and adaptive mutations. These mechanisms may accelerate evolution specifically when cells are maladapted to their environments, i.e., when they are are stressed. One mechanism of stress-induced mutagenesis in Escherichia coli occurs by error-prone DNA dou...

Abstract In order to solve the problems such as poor diversity and convergence ability of offspring population NSGA-II Algorithm in vehicle production scheduling problem, an improved shop algorithm based on is proposed. The NSGA-ii mainly focuses crossover mutation traditional Algorithm, proposes a new self-adaptive Crossover operator. By comparing individual crowding degree with average popula...

In this paper, the unrelated parallel batch processing machine (UPBPM) scheduling problem is addressed to minimize total energy consumption (TEC) and makespan. Firstly, a mixed-integer line programming model (MILP) of UPBPM presented. Secondly, self-adaptive multiobjective differential evolution (AMODE) algorithm put forward. Since parameter value can affect performance greatly, an adaptive con...

The presence of mutator genotypes in populations of bacteria may be favoured by selection because they produce rare beneficial mutations and thereby increase the rate of adaptive evolution. Recent work, however, shows that the relationship between mutation rates and adaptive evolution is more complicated.