In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much popularity in the evolutionary computation community for solving optimization problems. Characterized by the use of probabilistic models to represent the solutions and the interactions between the variables of the problem, EDAs can be applied to either discrete, continuous or mixed domain problems. Due to this robustness, these algorithms have been used to solve a diverse set of real-world and academic optimization problems. However, a straightforward application is only limited to a few cases, and for the general case, an efficient application requires intuition from the problem as well as notable understanding in probabilistic modeling. In this paper, we provide a roadmap for solving optimization problems via EDAs. It is not the aim of the paper to provide a thorough review of EDAs, but to present a guide for those practitioners interested in using the potential of EDAs when solving optimization problems. In order to present a roadmap which is as useful as possible, we address the key aspects involved in the design and application of EDAs, in a sequence of stages: (1) the choice of the codification, (2) the choice of the probability model, (3) strategies to incorporate knowledge about the problem to the model, and (4) balancing the diversification-intensification behavior of the EDA. At each stage, first, the contents are presented together with common practices and advice to follow. Then, an illustration is given with an example which shows different alternatives. In addition to the roadmap, the paper presents current open challenges when developing EDAs, and revises paths for future research advances in the context of EDAs.

近几十年来，分布算法估计 (EDA) 在进化计算社区中广受欢迎，用于解决优化问题。通过使用概率模型来表示问题的解决方案和变量之间的相互作用，EDA 可以应用于离散、连续或混合域问题。由于这种鲁棒性，这些算法已被用于解决各种现实世界和学术优化问题。然而，一个简单的应用程序仅限于少数情况，对于一般情况，一个有效的应用程序需要对问题的直觉以及对概率建模的显着理解。在本文中，我们提供了通过 EDA 解决优化问题的路线图。本文的目的不是对 EDA 进行全面审查，而是为那些有兴趣在解决优化问题时利用 EDA 潜力的从业者提供指南。为了提供尽可能有用的路线图，我们在一系列阶段解决了 EDA 设计和应用中涉及的关键方面：（1）编码的选择，（2）概率的选择模型，（3）将有关问题的知识纳入模型的策略，以及（4）平衡 EDA 的多样化 - 强化行为。在每个阶段，首先，将内容与常见做法和建议一起呈现。然后，通过一个示例给出了一个说明，该示例显示了不同的替代方案。除了路线图之外，本文还提出了开发 EDA 时当前面临的开放挑战，