提出了一种应用于软件测试中的基于模拟退火遗传算法的测试数据自动生成算法。
A kind of software test data automated generation method based on simulated annealing genetic algorithms is proposed.
提出了数组链染色体编码方式，以及基于自适应性变异概率和模拟退火惩罚函数法的适应性遗传算法（AGA）。
This paper proposes the array chain chromosome coding and the Adaptive GA (AGA) that combines the self-adaptive mutation probability and simulation anneal punishment function.
针对模拟退火和遗传算法的参数和操作选取问题，通过将其描述为随机优化问题，提出了基于OCBA的解决方法。
Aimed at the problem of selecting suitable parameters and operators for SA and GA, an OCBA based approach is proposed by formulating the considered problem as a stochastic optimization problem.
另一方面，通过将假设检验的统计思想融入智能优化算法，分别提出了基于假设检验的模拟退火和遗传算法。
In addition, hypothesis-test based simulated annealing (SA) and genetic algorithms are proposed respectively by incorporating the statistical idea of hypothesis test into intelligent algorithms.
论述了基于遗传算法的图像恢复方法，提出了将模拟退火法和遗传算法相结合求解图像非线性最优化问题的算法。
On the basis of introducing the existent blind deconvolution image recovery algorithm, a maximum likelihood image recovery algorithm deduced from probability statistical is presented in this paper.
论述了基于遗传算法的图像恢复方法，提出了将模拟退火法和遗传算法相结合求解图像非线性最优化问题的算法。
On the basis of introducing the existent blind deconvolution image recovery algorithm, a maximum likelihood image recovery algorithm deduced from probability statistical is presented in this paper.
应用推荐