Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey
作者：
Highlights：
• Data mining methods for extracting knowledge from multi-objective optimization are reviewed.
• Methods are classified based on the type and form of knowledge generated.
• Descriptive statistics, visual data mining and machine learning methods are discussed.
• Limitations of existing methods are discussed.
• A generic framework for knowledge-driven optimization is proposed.
摘要
•Data mining methods for extracting knowledge from multi-objective optimization are reviewed.•Methods are classified based on the type and form of knowledge generated.•Descriptive statistics, visual data mining and machine learning methods are discussed.•Limitations of existing methods are discussed.•A generic framework for knowledge-driven optimization is proposed.
论文关键词：Data mining,Multi-objective optimization,Descriptive statistics,Visual data mining,Machine learning,Knowledge-driven optimization
论文评审过程：Received 17 December 2015, Revised 9 October 2016, Accepted 10 October 2016, Available online 15 October 2016, Version of Record 21 November 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.015
