论文标题
用于任务可计算性模型的遗传优化算法
Genetic optimization algorithms applied toward mission computability models
论文作者
论文摘要
遗传算法是根据使用自然选择的生物进化过程建模的，以选择最佳物种来生存。它们是基于启发式方法的计算成本。遗传算法使用选择，交叉和突变来获得计算问题的可行解决方案。在本文中，我们将遗传优化算法描述为关键和约束意识到的计算问题。
Genetic algorithms are modeled after the biological evolutionary processes that use natural selection to select the best species to survive. They are heuristics based and low cost to compute. Genetic algorithms use selection, crossover, and mutation to obtain a feasible solution to computational problems. In this paper, we describe our genetic optimization algorithms to a mission-critical and constraints-aware computation problem.
