One of the most delicate stages of an evolutionary algorithm is the evaluation of the goodness of the solutions by some procedure providing a fitness value. However, although there are general rules, it is not always easy to find an appropriate evaluation function for a given problem. In the biological realm, today, there is a variety of experimental methods under the name of microbial screening to identify and select bacteria from their traits, as well as to obtain their fitness. In this paper, we show how given an optimization problem, a colony of synthetic bacteria or bacterial agents is able to evaluate the fitness of candidate solutions by building an evaluation function. The evaluation function is obtained simulating, in silico, a bacterial colony conducting the laboratory methods used in microbiology, biotechnology and synthetic biology to measure microbial fitness. Once the evaluation function is built, it is included in the code of the genetic algorithm as part of the fitness routine. The practical use of this approach is illustrated in two classic optimization problems. In silico routines have been programmed in Gro, a cell programming language oriented to synthetic biology, and can easily be customized to many other optimization problems.

中文翻译：

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进化算法适应度评估的计算机模拟微生物筛选法

进化算法最微妙的阶段之一是通过提供适应度值的某些过程来评估解的优劣。但是，尽管有通用规则，但为给定问题找到合适的评估函数并不总是那么容易。在生物领域中，如今，以微生物筛查为名的多种实验方法可以从细菌的特征中识别和选择细菌，并获得其适应性。在本文中，我们展示了给定一个优化问题时，合成细菌或细菌制剂的菌落如何通过构建评估函数来评估候选解决方案的适用性。通过在计算机上模拟执行微生物学实验方法的细菌菌落来获得评估功能，生物技术和合成生物学来测量微生物的适应性。一旦建立了评估功能，就将其作为适应性例程的一部分包含在遗传算法的代码中。在两个经典的优化问题中说明了该方法的实际使用。在计算机程序中，已经使用了针对合成生物学的细胞编程语言Gro进行了编程，并且可以轻松地针对许多其他优化问题进行自定义。