A Review of Evolutionary Optimization Methods for Information Visualization and Feature Space Exploration

Evolutionary optimization plays an important role in the representation of information from data sets originating from various technical fields and natural sciences, as it helps to explore parameter spaces for meaningful representations. Quality-Diversity (QD) methods, notably MAP-Elites variants, prove effective in diverse fields, emphasizing their ability to provide sets of high-performing solutions. This review discusses challenges in single- and multi-objective optimization, with applications in multiple directions such as image processing, visualization, and medical imaging. It also reviews QD algorithms and highlights advancements in algorithmic adaptations, user-driven optimization and the potential to explore complex feature spaces. The presented works contribute to understanding and applying evolutionary optimization for solving visualization and feature space exploration problems in various domains.