July 15-19, 2023

GEWS 2023

DESCRIPTION

Grammatical Evolution (GE) is an evolutionary algorithm that can be used to evolve program described by grammars. It does so by using a simple binary string to represent individuals, which are then mapped into more complex structure. Since it was first introduced 25 years ago, it has enjoyed continued popularity and it is now among the most popular variants of genetic programming. 18 years after the previous workshop, this year sees the reintroduction of the GEWs to celebrate this milestone.

The GEWS aims to present cutting-edge research and to be a premier forum for practitioners and researchers to discuss recent advances in GE and propose new research directions. 

All aspects of GE, including its foundations, expansions, analyses, applications, and latest software implementations, will be covered in the workshop. We welcome full-length and short-length research papers, and we especially encourage contributions of industry practice research.

A special issue of Springer's Genetic Programming & Evolvable Machines is planned from the workshop proceedings to celebrate 25 years of Grammatical Evolution. Dates and further information to come.

SUBMISSIONS

Workshop papers must be submitted using the GECCO submission site. The following types of papers are invited for submission:

Accepted workshop papers will be published as part of a companion volume to the conference proceedings in the ACM Digital Library.

The paper's format should follow the GECCO 2023 instructions.

 IMPORTANT DATES

Submission opening:   February 13, 2023

Submission deadline:   April 17, 2023 (Extended)

Acceptance notification:   May 3, 2023

Camera-ready and registration:   May 10, 2023

Author's mandatory registration:   May 10, 2023

Workshop date:   July 15 & 16, 2023

SCHEDULE

 ACCEPTED PAPERS





VENUE

GEWS2023 will be held as a part of the GECCO workshops at the Altis Grand Hotel, which is a Lisbon icon that has hosted many important events over the decades.

ORGANISERS

Conor Ryan | Website

Prof. Conor Ryan is a Professor of Machine Learning in the Computer Science and Information Systems (CSIS) department at the University of Limerick. He is interested in applying Machine Learning techniques to medical diagnosis, particularly in semi-automated mammography, and studied with Prof. Lásló Tabar in 2005 to obtain American Medical Association accreditation in Breast Cancer Screening. He also uses Machine Learning to perform data analytics on medical data (including so-called “Big Data”) to extract insights from large quantities of data. Current health-related projects include an Enterprise Ireland Commercialisation Fund project to develop a Stage 1 Breast Cancer Detection system, involving Cork University Hospital and the Royal Surrey County Hospital, as well as a longer-term project looking at cardiotocograph (CTG) interpretation.

Mahsa Mahdinejad | Website

Mahsa Mahdinejad is a Ph.D. student in artificial intelligence at the University of Limerick. Her research interests are Deep learning, Evolutionary Algorithms & Grammatical Evolution, Hybrid-Algorithms and Bioinformatics. She received her bachelor’s degree in Physics from Isfahan University of Technology. She also has worked as an Intern at the Department of Mathematics and Statistics, at the University of Limerick.

Aidan Murphy | Website

Dr. Aidan Murphy  received the bachelor’s degree in theoretical physics from the Trinity College Dublin, the H.Dip. degree in statistics from the University College Dublin, and the Ph.D. degree in explainable Artificial Intelligence (AI) (X-AI) from the BDS Laboratory, University of Limerick. He is currently a Postdoctoral Research Fellow with the Complex Software Laboratory, University College Dublin, researching software testing and mutation analysis. His research interests include grammatical evolution, transfer learning, fuzzy logic, and X-AI.