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researchers to discuss new and existing issues in these areas, and invite researchers to submit original and previously unpublished research and application papers.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
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.
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
Prof. Kangshun Li, South China Agricultural University, China
Dr. Feng Wang, Wuhan University, China