Abstract The increase of energy consumption and their direct effects on pollution and global warming have motivated governments to develop new strategies to promote a better usage of energy. One of the most important aspects related to energy efficiency is the need for a suitable model of energy consumption that can be used to make predictions or to aid experts in high level decision making processes. Symbolic regression techniques can be used to discover an energy consumption model that fits these purposes. Traditionally, the problem of symbolic regression has been solved by using genetic programming approaches to find the algebraic expression that best fits the regression problem data, where each expression is encoded as a tree structure. In previous works, we found that a different approach using Straight Line Programs as a representation technique could provide promising results for symbolic regression, although the size of the resulting algebraic expression might be increased when compared to the traditional approach. This work proposes an Ant Colony Optimization algorithm for Straight Line Programs to solve the problem, and makes a study to compare the approach with traditional genetic programming in a real energy consumption modelling problem.

中文翻译：

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使用直线程序进行符号回归的蚁群优化方法。在能耗建模中的应用

摘要 能源消耗的增加及其对污染和全球变暖的直接影响促使政府制定新的战略以促进更好地利用能源。与能源效率相关的最重要方面之一是需要一个合适的能源消耗模型，该模型可用于进行预测或帮助专家进行高层决策过程。符号回归技术可用于发现符合这些目的的能源消耗模型。传统上，符号回归的问题是通过使用遗传编程方法来找到最适合回归问题数据的代数表达式来解决的，其中每个表达式都被编码为树结构。在之前的作品中，我们发现使用直线程序作为表示技术的不同方法可以为符号回归提供有希望的结果，尽管与传统方法相比，生成的代数表达式的大小可能会增加。该工作提出了一种用于直线程序的蚁群优化算法来解决该问题，并在实际能耗建模问题中将该方法与传统遗传编程进行了比较。