Special Issue GPEM 2016 : Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation - Genetic Programming and Evolvable Machines (Springer)
Call For Papers
Genetic Programming and Evolvable Machines
~Call for Papers~
Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation
Scheduling and combinatorial optimisation problems appear in many practical applications in production and service industries and have been the research interest of researchers from operations research and computer science. These problems are usually challenging in terms of both complexity and dynamic changes, which requires the development of innovative solution methods. Although the research in this field has made a lot of progress, designing effective algorithms/heuristics for scheduling and combinatorial optimisation problems is still a hard and tedious task. In the last decade, there has been a growing interest in applying computational intelligence (particularly evolutionary computation) techniques to help facilitate the design of scheduling algorithms and many state-of-the-art methods have been developed.
This special issue aims to present the most recent advances in scheduling and combinatorial optimisation with a special focus on automated heuristic design and self-adaptive algorithms. This includes (1) offline approaches to automatically discover new and powerful algorithms/heuristics for scheduling and combinatorial optimisation problems, and (2) online approaches which allow scheduling algorithms to self- adapt during the solving process. We encourage papers employing variable-length representations for scheduling algorithms. Here are a number of potential techniques which are highly relevant to this special issue:
Hyper-heuristics for heuristic/operator selection
Hyper-heuristics for generating new operators and algorithms
Evolutionary design of heuristics
Self-adaptive evolutionary algorithms
Machine learning-based meta-heuristics
Learning classifier systems
Scheduling or optimisation of algorithms and machines
Topics of interest include, but are not limited to:
2D/3D strip packing
Automated heuristic design
Innovative applications of scheduling and combinatorial optimisation
Web service composition
Wireless networking state location allocation
Airport runway scheduling
Submission deadline: Oct. 1, 2016
Notification of first review: December 1, 2016
Resubmission: January 20, 2017
Final acceptance notification: February 20, 2017
Manuscripts should be submitted to: http://GENP.edmgr.com. Please choose “Automated Design and Adaptation” as the article type when submitting.
Dr. Su Nguyen, Victoria University of Wellington, New Zealand
Dr. Yi Mei, Victoria University of Wellington, New Zealand (email@example.com)
Dr. Mengjie Zhang, Victoria University of Wellington, New Zealand (Mengjie.Zhang@ecs.vuw.ac.nz)
 J. Branke, S. Nguyen, C. W. Pickardt, and M. Zhang, “Automated Design of Production Scheduling Heuristics: A Review,” IEEE Trans. Evol. Comput., vol. 20, no. 1, pp. 110–124, Feb. 2016.
 E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and J. R. Woodward, “Exploring Hyper-heuristic Methodologies with Genetic Programming,” in Computational Intelligence, vol. 1, C. Mumford and L. Jain, Eds. Springer Berlin Heidelberg, 2009, pp. 177–201.
 L. Feng, Y.-S. Ong, M.-H. Lim, and I. W. Tsang, “Memetic Search With Interdomain Learning: A Realization Between CVRP and CARP,” IEEE Trans. Evol. Comput., vol. 19, no. 5, pp. 644–658, Oct. 2015.
 G. Kendall and N. M. Hussin, “A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology,” in Practice and Theory of Automated Timetabling V, vol. 3616, E. Burke and M. Trick, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 270–293.
 N. R. Sabar, M. Ayob, G. Kendall, and Rong Qu, “Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems,” IEEE Trans. Evol. Comput., vol. 19, no. 3, pp. 309–325, Jun. 2015.
 J. H. Drake, E. Özcan, and E. K. Burke, “A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem,” Evol. Comput., vol. 24, no. 1, pp. 113–141, Mar. 2016.