About the GPEMjournal blog

This is the editor's blog for the journal Genetic Programming and Evolvable Machines. The official web site for the journal, maintained by the publisher (Springer) is here. The GPEMjournal blog is authored and maintained by Lee Spector.

Tuesday, May 31, 2016

GPEM 17(2) is now available

The second issue of Volume 17 of Genetic Programming and Evolvable Machines is now available for download.

It contains:

"Partial-DNA cyclic memory for bio-inspired electronic cell"
by Sai Zhu, Jin-yan Cai, and Ya-feng Meng

"Grammar-based generation of variable-selection heuristics for constraint satisfaction problems"
by Alejandro Sosa-Ascencio, Gabriela Ochoa, Hugo Terashima-Marin, and Santiago Enrique Conant-Pablos

"A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization"
by Behnaz Moradabadi, Mohammad Mahdi Ebadzadeh, and Mohammad Reza Meybodi

"Evolutionary design of complex approximate combinational circuits"
by Zdenek Vasicek and Lukas Sekanina

"Anthony Brabazon, Michael O’Neill, Sean McGarraghy: Natural computing algorithms"
by Simone A. Ludwig

"Gusz Eiben and Jim Smith (Eds): Introduction to evolutionary computing"
by Jeffrey L. Popyack

"Erratum to: Gusz Eiben and Jim Smith: Introduction to evolutionary computing (second edition)"
by Jeffrey L. Popyack

CFP: Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation

Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation

Call for Papers

Guest Editors:

Dr. Su Nguyen, Victoria University of Wellington, New Zealand (su.nguyen@ecs.vuw.ac.nz)

Dr. Yi Mei, Victoria University of Wellington, New Zealand (yi.mei@ecs.vuw.ac.nz)

Dr. Mengjie Zhang, Victoria University of Wellington, New Zealand (Mengjie.Zhang@ecs.vuw.ac.nz)

For a formatted PDF version of this CFP click here.

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
- Memetic algorithms
- Genetic programming
- 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:

- Production scheduling
- Timetabling
- Vehicle routing
- Grid/cloud scheduling
- 2D/3D strip packing
- Space/resource allocation
- Automated heuristic design
- Innovative applications of scheduling and combinatorial optimisation
- Web service composition
- Wireless networking state location allocation
- Airport runway scheduling
- Project scheduling
- Traffic control

Paper Submission

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.

Springer offers authors, editors and reviewers of Genetic Programming and Evolvable Machines a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript with straightforward log-in and submission procedures, and it supports a wide range of submission file formats.

Manuscripts should be submitted to: http://GENP.edmgr.com.

Choose “Automated Design and Adaptation” as the article type when submitting.

Important Dates

Submission deadline: Oct. 1, 2016 Extended to October 15, 2016
Notification of first review:December 1, 2016
Resubmission: January 20, 2017
Final acceptance notification: February 20, 2017


[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.