Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in particular are well known tools for successful optimization of difficult problems. But when is their application meaningful and how does one approach such a project as a novice? How do you avoid beginner's mistakes or use the design possibilities of a metaheuristic search as efficiently as possible? This paper tries to give answers to these questions based on 30 years of research and application of the Evolutionary Algorithm GLEAM and its memetic extension HyGLEAM. Most of the experience gathered and discussed here can also be applied to the use of other metaheuristics such as ant algorithms or particle swarm optimization. This paper addresses users with basic knowledge of MHs in general and EAs in particular who want to apply them in an optimization project. For this purpose, a number of questions that arise in the course of such a project are addressed. At the end, some non-technical project management issues are discussed, whose importance for project success is often underestimated.

中文翻译：

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成功应用进化算法：从实际应用中获得的指南

一般而言，元启发式 (MH) 和进化算法 (EA) 尤其是成功优化困难问题的众所周知的工具。但是他们的应用程序什么时候有意义，作为新手如何处理这样的项目？您如何避免初学者的错误或尽可能有效地使用元启发式搜索的设计可能性？本文试图基于进化算法 GLEAM 及其模因扩展 HyGLEAM 30 年的研究和应用来回答这些问题。这里收集和讨论的大部分经验也可以应用于其他元启发式算法的使用，例如蚂蚁算法或粒子群优化。本文面向具有一般 MH 基本知识和特别是 EA 基础知识的用户，他们希望将它们应用到优化项目中。以此目的，解决了此类项目过程中出现的一些问题。最后，讨论了一些非技术性的项目管理问题，它们对项目成功的重要性往往被低估。