A Survey on Evolutionary Multimodal Optimization-Driven Machine Learning
这篇综述首次全面回顾了进化多解/多模态优化在经典机器学习任务中的应用，分析了学习任务的多解特性，并讨论了未来发展方向。
Machine learning methods have obtained great success across a number of practical applications and attracted growing interest. How to improve learning performance has emerged as a prominent research area. As an important area in evolutionary computation, evolutionary multi-solution/multimodal optimization has been widely used for dealing with different learning tasks. The key attention is to find multiple optimal solutions/models using the population-based search mechanism. This survey presents a very first comprehensive review of applying evolutionary multi-solution/multimodal optimization for addressing classical machine learning tasks and the fundamental concepts related to this domain. In this context, the multi-solution characteristics of a learning task, i.e., different solutions/models with very similar or the same performance, are emphasized and analyzed. Additionally, successful applications with multi-solution characteristics are presented. This work also discusses key challenges and provides insights into potential emerging directions for the future developments of applying evolutionary multimodal optimization to machine learning.