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.

Wednesday, May 22, 2019

GPEM 20(2) is now available

The second issue of Volume 20 of Genetic Programming and Evolvable Machines is now available for download.

It contains:

On the scalability of evolvable hardware architectures: comparison of systolic array and Cartesian genetic programming
by Javier Mora, Rubén Salvador & Eduardo de la Torre

Vector quantization using the improved differential evolution algorithm for image compression
by Sayan Nag

EvoParsons: design, implementation and preliminary evaluation of evolutionary Parsons puzzle
by A. T. M. Golam Bari, Alessio Gaspar, R. Paul Wiegand, Jennifer L. Albert, Anthony Bucci & Amruth N. Kumar

A multi-level grammar approach to grammar-guided genetic programming: the case of scheduling in heterogeneous networks
by Takfarinas Saber, David Fagan, David Lynch, Stepan Kucera, Holger Claussen & Michael O’Neill

Hitoshi Iba: Evolutionary approach to machine learning and deep neural networks: neuro-evolution and gene regulatory networks
by Petra Vidnerová

Sunday, March 31, 2019

GPEM 20(1) is now available

The first issue of Volume 20 of Genetic Programming and Evolvable Machines is now available for download.

It contains:

Editorial introduction
by Lee Spector

Acknowledgment to reviewers
by Lee Spector

DENSER: deep evolutionary network structured representation
by Filipe Assunção, Nuno Lourenço, Penousal Machado & Bernardete Ribeiro

Designing automatically a representation for grammatical evolution
by Eric Medvet, Alberto Bartoli, Andrea De Lorenzo & Fabiano Tarlao

A genetic programming approach for delta hedging
by Zheng Yin, Anthony Brabazon, Conall O’Sullivan & Philip A. Hamill

Evolving continuous cellular automata for aesthetic objectives
by Jeff Heaton

Automated discovery of test statistics using genetic programming
by Jason H. Moore, Randal S. Olson, Yong Chen & Moshe Sipper

Software review: DEAP (Distributed Evolutionary Algorithm in Python) library
by Jinhan Kim & Shin Yoo

Georgios N. Yannakakis and Julian Togelius: Artificial Intelligence and Games
by Pablo García-Sánchez

Evelyne Lutton, Nathalie Perrot, Alberto Tonda: Evolutionary algorithms for food science and technology
by Kelly Androutsopoulos

Tuesday, February 12, 2019

Call For Entries: 16th Annual (2019) "Humies" Awards

Quick reminder the Annual  "Humies" Awards featuring cash prizes totalling $10000 for Human-Competitive Results will again be held at GECCO-2019.
This year GECCO will be July 13th-17th, 2019 (Saturday - Wednesday) in Prague, Czech Republic see http://gecco-2019.sigevo.org.

Your human competitive entries (multiple entries are allowed) must be submitted by Wednesday 5 June 2019 by email to goodman at msu dot edu   Full details of how to enter can be found via http://www.human-competitive.org


Wednesday, January 23, 2019

Deadline extension: special issue on Integrating Numerical Optimization Methods with Genetic Programming

The deadline for submissions for the special issue on "Integrating Numerical Optimization Methods with Genetic Programming" has been extended.

The revised timeline is:

Submission deadline: February 20, 2019
Notification of first review: May 22, 2019
Resubmission: June 19, 2019
Final acceptance notification: August 14, 2019

Friday, September 14, 2018

GPEM 19(4) is now available

The fourth issue of Volume 19 of Genetic Programming and Evolvable Machines is now available for download.

It contains:

Grammatical evolution as a hyper-heuristic to evolve deterministic real-valued optimization algorithms
by Iztok Fajfar, Árpád Bűrmen & Janez Puhan

Self-adaptive multi-population genetic algorithms for dynamic resource allocation in shared hosting platforms
by Azam Shirali, Javidan Kazemi Kordestani & Mohammad Reza Meybodi

Comparison of semantic-based local search methods for multiobjective genetic programming
by Tiantian Dou & Peter Rockett

Alain Pétrowski and Sana Ben-Hamida: Evolutionary Algorithms
by Keith Downing

Kathryn E. Merrick: Computational models of motivation for game-playing agents
by Spyridon Samothrakis

Ryan J. Urbanowicz and Will N. Browne: Introduction to learning classifier systems
by Analía Amandi

GPEM 19(3) is now available

The third issue of Volume 19 of Genetic Programming and Evolvable Machines, a special issue on genetic programming, evolutionary computation and visualization, edited by Nadia Boukhelifa & Evelyne Lutton, is now available for download.

It contains:

Guest editorial: Special issue on genetic programming, evolutionary computation and visualization
by Nadia Boukhelifa & Evelyne Lutton

Visualising the global structure of search landscapes: genetic improvement as a case study
by Nadarajen Veerapen & Gabriela Ochoa

Unveiling evolutionary algorithm representation with DU maps
by Eric Medvet, Marco Virgolin, Mauro Castelli, Peter A. N. Bosman, Ivo Gonçalves & Tea Tušar

Data exploration in evolutionary reconstruction of PET images
by Cameron C. Gray, Shatha F. Al-Maliki & Franck P. Vidal

Visualisation with treemaps and sunbursts in many-objective optimisation
by David J. Walker

VALIS: an evolutionary classification algorithm
by Peter Karpov, Giovanni Squillero & Alberto Tonda

Wednesday, August 1, 2018

CFP: Integrating Numerical Optimization Methods with Genetic Programming

[Full CFP on the Journal's Springer site]

Guest Editors

About this Issue

This special issue focuses on integrating numerical optimization methods with Genetic Programming (GP) in order to improve the evolutionary search. In traditional GP the search space is the space of all possible syntactic expressions that can be generated from the set of functions and terminals, which depends upon the type of program representation used. The search operators modify individuals at the level of syntax, and, given that syntactic expressions tend to be fragile, their effect on behavior is usually non-local and difficult to predict. This has lead researchers to explore other search operators or program representations.

One possibility is to use numerical optimization methods as a local search process. In fact, the representation and adaptation (i.e. learning) of real-valued parameters in GP is still an open issue in GP at large, where most of the work has focused on what Koza termed ephemeral random constants and some effort has been devoted to adapting them [1]. Although more advanced approaches such as adaptive node gains were proposed over two decades ago [2] and their uptake has produced some success [3] [4], it has been relatively limited [5]. This area is particularly relevant in modern machine learning, where powerful computing platforms like GPUs are highly optimized for performing such tasks. Numerical optimization can also be used to tune hyper-parameters or to derive surrogate models on-line.

This special issue intends to explore new representations, algorithms and methodologies that can enhance GP systems by exploiting numerical optimization techniques to improve convergence, reduce computation cost and achieve state-of-the- art performance in real-world machine learning challenges [6], and in particular in Deep Learning, a field in which Genetic Programming is becoming increasingly successful [7].


  • Novel representations that are amenable to numerical and local search methods
  • Search approaches for real-valued parameters or meta-parameters in GP individuals
  • New search operators that can exploit both syntactic and numerical search
  • Implementations that improve search efficiency and reduce training times
  • Techniques that are optimized for High Performance Computing platforms, such as GPUs and FPGAs

Important Dates

  • Submission deadline: January 20, 2019 
  • Notification of first review: May 2, 2019 
  • Resubmission: June 3, 2019
  • Final acceptance notification: August 2, 2019

Submissions and Review Procedures

Special Issues are handled in the normal way via the online Editorial Manager system found at https://genp.edmgr.com. Please choose the article type “Integrating Numerical Optimization Methods with Genetic Programming.” Special Issue articles should fulfil all the standard requirements of any GPEM article. Authors should note that the same criteria apply to articles in Special Issues as to regular articles. Special Issue articles must not consist of overviews of the authors' previously published work, e.g. peer- reviewed articles, book chapters, official reports, etc.

All papers will undergo the same rigorous GPEM review process. Please refer to the GPEM website for detailed instructions on paper submission: http://www.springer.com/10710


[1] L. M. Howard and D. J. D'Angelo, "The GA-P: a genetic algorithm and genetic programming hybrid," in IEEE Expert, vol. 10, no. 3, pp. 11-15, Jun 1995. doi: http://dx.doi.org/10.1109/64.393137

[2] Esparcia-Alcázar A.I., Sharman K.C. (1996) Genetic programming techniques that evolve recurrent neural network architectures for signal processing, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop, pages 139-148 DOI: http://dx.doi.org/10.1109/NNSP.1996.548344

[3] Maarten Keijzer. 2004. Scaled Symbolic Regression. Genetic Programming and Evolvable Machines 5, 3 (September 2004), 259-269. DOI: http://dx.doi.org/10.1023/B:GENP.0000030195.77571.f9

[4] Emigdio Z-Flores, Leonardo Trujillo, Oliver Schütze, and Pierrick Legrand. 2015. A Local Search Approach to Genetic Programming for Binary Classification. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO '15), Sara Silva (Ed.). ACM, New York, NY, USA, 1151-1158. DOI: http://dx.doi.org/10.1145/2739480.2754797

[5] Leonardo Trujillo, Emigdio Z-Flores, Perla S. Juárez Smith, Pierrick Legrand, Sara Silva, Mauro Castelli, Leonardo Vanneschi, Oliver Schütze and Luis Muñoz. Local Search is Underused in Genetic Programming. In Rick Riolo et al. editors, Genetic Programming Theory and Practice XIV, Ann Arbor, USA, 2017. Springer.

[6] Numerical and Evolutionary Optimization Workshop: http://neo.cinvestav.mx/NEO2018/

[7] Risto Miikkulainen, Evolving Multitask Neural Network Structure, Metalearning Symposium at NIPS 2017, http://metalearning-symposium.ml/files/miikkulainen.pdf