** CFP: Special Issue on Genetic Programming, Evolutionary Computation and Visualization ** Recent advances in Genetic Programming / Evolutionary Computation (GP/EC) and visualization have shown a growing synergy between these fields. On the one hand, visualization can make GP/EC algorithms more transparent, through interactive parameter tuning and the graphical representation of experimental analyses [3,5]. On the other hand, GP/EC techniques can be deployed to efficiently guide user search in exploratory visualization systems [1,2,4]. This special issue aims to bring together latest work and new advances in Genetic Programming / Evolutionary Computation (GP/EC) and Visualization (including its three subfields: information visualization, scientific visualization, and visual analytics), to produce new insights on: - how to develop better visualization tools to support GP/EC, and - how to develop robust GP/EC algorithms to support visualization applications. Scope -------- We invite submissions on any aspect of Genetic Programming and Evolutionary Computation (GP/EC) linked to visualization (information visualization, scientific visualization or visual analytics), or visualization applied to GP/EC, including, but not limited to: theoretical results, algorithmic design, evaluations and user studies, surveys, and interesting new applications (e.g. in life sciences, art, and engineering). Suggested topics include, but are not limited to: * Interactive evolution systems * GP/EC applied to visual design * Use of GP/EC in visual analytics or progressive visualization * Review of visualization techniques in GP/EC * Review of GP/EC techniques in visualization * Visualization techniques to explain GP/EC to end users * Visual analysis of GP/EC algorithms * Evaluation of (interactive) evolutionary systems * Visualization of results of experimental analysis of GP/EC algorithms * Interactive parameter tuning of GP/EC * Visualization of the search space and the fitness landscape * Techniques to avoid user fatigue in interactive GP/EC * Human factors in GP/EC Important Dates ----------------------- GPEM Special Issue Submission Deadline: 15 December 2017 First Reviews: March 2018 Post Review Submission Deadline: April 2018 Acceptance Notification: June 2018 Camera-ready Paper Deadline: July 2018 Paper Submission -------------------------- Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals. Manuscripts should be submitted to: http://GENP.edmgr.com <http://genp.edmgr.com/>. Choose “Genetic Programming, Evolutionary Computation and Visualization” as the article type when submitting. Manuscripts should conform to the standard format stipulated in Genetic Programming and Evolvable Machines’ Information for Authors. All submissions will be peer reviewed subject to the standards of the journal. All inquiries should be sent to: nadia.boukhelifa@inra.fr <mailto:nadia.boukhelifa@inra.fr> Guest Editors -------------------- Nadia Boukhelifa, INRA, Versailles-Grignon, France [nadia.boukhelifa@inra.fr <mailto:nadia.boukhelifa@inra.fr>] Evelyne Lutton, INRA, Versailles-Grignon, France [evelyne.lutton@inra.fr <mailto:evelyne.lutton@inra.fr>] References ----------------- [1] N. Boukhelifa, A. Bezerianos, W. Cancino, and E. Lutton. 2017. Evolutionary visual exploration: evaluation of an IEC framework for guided visual search. Evol. Comput. 25, 1 (March 2017), 55-86. DOI:https://doi.org/10.1162/EVCO_a_00161 [2] N. Boukhelifa, A. Bezerianos, and E. Lutton. A mixed approach for the evaluation of a guided exploratory visualization system. 2015. EuroVis workshop on reproducibility, verification, and validation in visualization, EuroRV3. [3] E. Lutton, and J-D Fekete. Visual analytics of EA data. 2011. Genetic and Evolutionary Computation Conference, GECCO. [4] W. Cancino, N. Boukhelifa, and E. Lutton. EvoGraphDice: Interactive evolution for visual analytics. 2012. IEEE Congress on Evolutionary Computation, 1-8. [5] T. Collins. Visualizing evolutionary computation. Advances in evolutionary computing, Ashish Ghosh and Shigeyoshi Tsutsui (Eds.). 2003. Springer-Verlag New York, Inc, 95-116.