oneillm – Page 2 – UCD Natural Computing Research & Applications Group

New Research Scientist

Dr Dipak Sharma joins the UCD Natural Computing Research & Applications Group as a Research Scientist on the DTIF GUARD project. Dr Sharma joins us from ACSL Ltd in Tokyo, Japan where he was employed as R&D Engineer – AI Lead.

New Faculty

We are delighted to Welcome a new Faculty member, Dr Mark Connor, to the UCD Natural Computing Research and Applications Group, and to the Management Information Systems Subject Area in the UCD College of Business. Mark joins us from his previous post as Lecturer in Sport Performance Analysis at the University of Suffolk. Prior to this Mark’s roles include Senior Data Scientist with EY, and Research & Innovation Lead with STATSports.

Adaptive Athlete Training Plan Generation

Our paper “Adaptive Athlete Training Plan Generation: An Optimal Control Systems Approach” has been published in the Journal of Science and Medicine in Sport, Volume 25 Issue 4.

We address the problem of automatically adapting athlete training plans using approaches from control systems theory and artificial intelligence, comparing a novel evolutionary computation approach, to proportional adjustment, and a pseudo-random control over simulations that replicate real-World training scenarios.

Optimizing Team Sport Training With Multi-Objective Evolutionary Computation

A collaboration between the UCD Natural Computing Research and Applications Group and STATSports has resulted in a journal publication introducing a novel method for mathematically optimising team sport training models using evolutionary computation.

Connor M., Fagan D., Watters B., McCaffery F., O’Neill M. (2021). Optimizing Team Sport Training With Multi-Objective Evolutionary Computation. International Journal of Computer Science in Sport, 20(1):92-105

Lero Director’s Prize for Research Excellence 2021

Congratulations to Prof Michael O’Neill who was awarded the Lero Director’s Prize for Research Excellence 2021. The annual awards recognise the enormous commitment and contribution of Lero members to the research centre’s success.

On receiving the award Prof O’Neill said “Thank You to Lero, and thank you to everyone who contributed to our shared research success these 20+ years, especially all past and present members of the UCD Natural Computing Research and Applications Group, especially Tony Brabazon, the UCD School of Business, UCD School of Computer Science, UL School of Computer Science and Information Systems, and co-inventors of Grammatical Evolution, Conor Ryan and J.J. Collins who are joint winners of this award“.


10 years on from hosting GECCO 2011 in Dublin, Ireland, Mark Connor and Michael O’Neill presented an approach to optimising the Banister Dose-Response Fitness-Fatique model used in athlete training management (arxiv draft) at GECCO 2021


Michael presented an invited talk on “Grammars, Evolutionary Computation and Intepretability” in a special session on Grammatical Evolution at DSSV-ECDA 2021 (7-9 July, 2021). The session was organised by Andreas Geyer-Schulz who spoke on “Architectural Design of a Unified GA/GP Package for R”, and Franz Rothlauf gave a talk on “Program Synthesis with Grammatical Evolution”.

Grammatical evolution for constraint synthesis for mixed-integer linear programming

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. 

Data-driven time series feature extraction

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.

Figure 9 from Mauceri et al., 2021 is licensed under CC BY 4.0
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