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Combining Evolutionary Algorithms with Constraint Solving for Configuration Optimization

机译：将进化算法与配置优化的约束求解

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In Search based Software Engineering, well-known evolutionary algorithms are utilized to find the optimal solutions and address the configuration optimization problem for software product lines and trade off multiple often competing objectives. Previous work by Henard et al. showed the weakness of the constraint expressiveness and the optimality and speed. In this work, we propose a multi-objective evolutionary algorithm, which significantly improves the expressiveness from Boolean constraints to quantifier-free first-order constraints, particularly without sacrificing much performance. Furthermore, we propose a parallel portfolio approach. Empirical results demonstrate that this approach presents the performance superiority compared with the state-off-the-art and improves optimality as far as possible within a limited time budget. Finally, we present an overview of challenges in future.
机译：在基于搜索的软件工程中，利用了众所周知的进化算法来查找最佳解决方案，并解决软件产品线的配置优化问题，并折衷多个竞争目标。以前的工作Henard等。表现出限制性弱点和最优性和速度。在这项工作中，我们提出了一种多目标进化算法，这显着提高了从布尔限制到无量子的一流约束的表现力，特别是在不牺牲大量性能。此外，我们提出了一种并行组合方法。经验结果表明，与现有技术相比，这种方法呈现性能优势，并在有限的时间预算中尽可能改善最优性。最后，我们将来概述了挑战。

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