Genetic Algorithms have established their capability for solving many complex
optimization problems. Even as good solutions are produced, the user's
understanding of a problem is not necessarily improved, which can lead to a
lack of confidence in the results. To mitigate this issue, explainability aims
to give insight to the user by presenting them with the knowledge obtained by
the algorithm. In this paper we introduce Partial Solutions in order to improve
the explainability of solutions to combinatorial optimization problems. Partial
Solutions represent beneficial traits found by analyzing a population, and are
presented to the user for explainability, but also provide an explicit model
from which new solutions can be generated. We present an algorithm that
assembles a collection of Partial Solutions chosen to strike a balance between
high fitness, simplicity and atomicity. Experiments with standard benchmarks
show that the proposed algorithm is able to find Partial Solutions which
improve explainability at reasonable computational cost without affecting
search performance.

通过引入部分解决方案来改善组合优化问题的解释能力，我们提出了一种算法，该算法选择一系列部分解决方案以在高适应度、简单性和原子性之间取得平衡，实验证明该算法能够以合理的计算成本找到改善解释能力的部分解决方案而不影响搜索性能。