Theory and Applications for Learning Guided Evolutionary Optimization and Fitness Landscape Analysis

Evolutionary algorithms are highly flexible in terms of handling constraints, dynamic changes, and multiple conflicting objectives. In real-world applications, many complex optimization problems do not have an analytical objective function available during the optimization process. Learning guided evolutionary optimization (LGEO) utilizes statistical and machine learning techniques to assist the evolutionary algorithms. The area of LGEO has attracted attention of researchers over the recent years due to its applicability and interesting computational aspects. With the growth of new technologies and models in machine learning, researchers in this field have to continuously face with new challenges, such as which learning techniques can be used and how to use learning techniques to help design optimization algorithms.

Being Influenced by biological evolution, researchers began the fitness landscape research early in the field of evolutionary optimization, whose purpose is to understand the behavior of evolutionary algorithms to solve optimization problems. Fitness landscape analysis (FLA) can be used to many real-world problems by analyzing the underlying search space in terms of the objectives to be optimized. There have been many recent advances in the FLA field in the development of methods and measures that have been shown to be effective in the understanding of algorithm behavior, the prediction of meta-heuristic performance and the selection of algorithms..

This special issue aims to provide a platform for bringing together researchers to discuss new and existing issues in these areas, and invite researchers to submit original and previously unpublished research and application papers.

Topics of Interest:

Topics include, but are not limited to the following:

•  Therotical analysis on learning guided evolutionary computation
•  Therotical analysis on fitness landscape analysis
•  Evolutionary learning methods on scheduling problems
•  Learning guided evolutionary strategy design
•  Fitness landscape analysis techniques for evolutionary algorithms
•  Advanced data-driven evolutionary algorithms
•  Multi-objective data-driven optimization methods
•  Surrogate models in evolutionary algorithms
•  Deep learning in learning guided evolutionary optimization
•  Knowledge mining techniques for learning guided evolutionary optimization problems
•  Learning guided evolutionary alogrithms in scheduling optimization
•  Fitness landscape analysis techniques for continuous optimisation problems
•  Learning guided evolutionary alogrithms in dynamic/real-time/nondeterministic systems

Submission:

All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). Submissions must be directly sent via the INS submission web site at https://www.journals.elsevier.com/information-sciences.

Important Dates

Submission of manuscripts: Nov. 31, 2019

Notification of review results: Feb.29, 2020

Revised version submission: Mar. 31, 2020

Acceptance notification: Apr. 30, 2020

Final manuscripts due: May 31, 2020

Anticipated publication: 2020

Guest Editors:

Prof. Kangshun Li, South China Agricultural University, China

Email: likangshun@sina.com

Dr. Feng Wang, Wuhan University, China

Email: fengwang@whu.edu.cn