Author: oneillm

Handcrafting mixed-integer linear programming (MILP) models can be a time-consuming and error-prone task. A novel algorithm, Grammatical Evolution for Constraint Synthesis (GECS), has been proposed which produces well-formed MILP models in the ZIMPL modelling language. GECS outperform state-of-the-art algorithms, and appears resistant to the curse of dimensionality. The research collaboration between Dr Tomasz Pawlak (Poznan University of Technology) and Prof Michael O’Neill (UCD Natural Computing Research & Applications Group) has been published in the journal Swarm and Evolutionary Computation.

Pawlak T., O’Neill M. (2021). Grammatical Evolution for Constraint Synthesis for Mixed-Integer Linear Programming. Swarm and Evolutionary Computation, 64:100896. 

Michael and Tony presented new research on the role of Social Learning in the search of variable length structures at CEC 2021. A draft is available on arxiv.

Stefano Mauceri and co-authors (James Sweeney, Miguel Nicolau and James McDermott) have published their latest research on a data-driven approach to feature extraction from time series for one-class classification in the journal Genetic Programming & Evolvable Machines. The approach, which uses Grammatical Evolution to automatically select both the features to extract and the sub-sequences from which to extract them, is demonstrated to lead to problem understanding and improved performance.

David Lynch and Michael O’Neill with NCRA alumnus James McDermott have been nominated for the Best Paper Award at the PPSN 2020 conference to be held in Leiden in September. The paper brings together the use of grammars and autoencoders in a novel approach to program synthesis.

Lynch D., McDermott J., O’Neill M. (2020). Program Synthesis in a Continuous Space using Grammars and Variational Autoencoders. The Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI). Springer LNCS.

Miguel Nicolau with NCRA alumnus Alexandros Agapitos have published their latest research on function sets, generalisation and symbolic regression in the Genetic Programming & Evolvable Machines journal.

Nicolau, M., Agapitos, A. (2020). Choosing function sets with better generalisation performance for symbolic regression models. Genetic Programming and Evolvable Machines. https://doi.org/10.1007/s10710-020-09391-4

NCRA researchers (Róisín Loughran, Tony Brabazon and Michael O’Neill) have published three articles in the 20th Anniversary issue of the journal Genetic Programming & Evolvable Machines. Two articles provide an overview of research in application areas we have been focusing on as a group for sometime, Finance & Economics (https://rdcu.be/b4KkQ), and Computational Creativity (https://rdcu.be/b4KkT). The third article, “Automatic Programming: The Open Issue?” (https://rdcu.be/b4KkV) follows on from an article by O’Neill et al that appeared in the 10th Anniversary issue highlighting Open Issues in the field of GP, and raises a challenge to the community to re-ignite a focus on Automatic Programming, the open issue, which we previously referred to as the “elephant in the room”. 

Michael O’Neill was interviewed for three articles (on What is Artificial Intelligence?,   Can Artificial Intelligence be creative?, and Artificial Intelligence in Sport) as part of an Irish Times Special Report on Artificial Intelligence.

Congratulations to Dr David Lynch and Dr Stefan Forstenlechner who graduated with their PhD from UCD today. David’s thesis explored “Automated self-optimisation in heterogeneous wireless communications networks using techniques from evolutionary computation“, and Stefan’s focused on “Program Synthesis with Grammars and Semantics in Genetic Programming“.

 

Our latest research published today in the Journal of Business Analytics shows evidence to support increased Business Analytics capability levels generate enhanced organisational value and competitive advantage. We  find that to drive organisational value and competitive advantage investments should be prioritised towards People first, then Governance, Culture and before finally Technology. Based on our findings we propose a new definition for Business Analytics.