The book "Multi-objectivization in Evolutionary Algorithms" by Darrell Lochtefeld provides a comprehensive examination of the process of multi-objectivization in evolutionary algorithms. It addresses the reformulation of single-objective problems into multi-objective problems and the application of corresponding methods to solve these issues. The author develops a classification framework for the diversity of evolutionary algorithms and analyzes various techniques of multi-objectivization. By investigating concrete problems such as the job shop scheduling problem and the traveling salesman problem, fundamental principles for designing multi-objectivization approaches are identified. The book offers both analytical and experimental comparisons of common multi-objectivization techniques and proposes a more general version of these techniques that is characterized by robust performance across different computational budgets.