Profile

Olivier Regnier-Coudert
Title: Dr
First Name: Olivier
Surname: Regnier-Coudert
Position: Senior Research Fellow
Telephone: +44 (0) 1224 262541
Email:
ORCID: ORCID Icon http://orcid.org/0000-0002-1143-6251

Member of the Computational Intelligence Group at the Robert Gordon University.

Research Interests

  • Data modelling: probabilistic modelling using Bayesian Networks and Markov Networks
  • Optimization: Evolutionary Algorithms, Estimation of Distribution Algorithms, fitness modelling, fitness landscape analysis
  • Medical applications: prostate cancer staging, chemotherapy scheduling
  • Industrial applications: truck and vessel scheduling

Publications

Journal papers

  • A. Brownlee, O. Regnier-Coudert, J. McCall, S. Massie, S. Stulajter. An application of a GA with Markov network surrogate to feature selection. International Journal of System Sciences, 2013. Vol. 44 (11): 2039-2056
  • T. Lam, O. Regnier-Coudert, J. McCall, S. McClinton. Development and validation of a UK-specific prostate cancer staging predictive model: UK prostate cancer tables. British Journal of Medical and Surgical Urology, 2012. Vol. 5 (5): 224-235
  • O. Regnier-Coudert, J. McCall, R. Lothian, T. Lam, S. McClinton, J. N’ Dow. Machine learning for improved pathological staging of prostate cancer: A performance comparison on a range of classifiers. Artificial Intelligence in Medicine, 55(1):25-35. 2012.

Conference papers

  • O. Regnier-Coudert, J. McCall, M. Ayodele: Geometric-based sampling for permutation optimization. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2013), 2013: 399-406
  • O. Regnier-Coudert, J. McCall: Competing mutating agents for bayesian network structure learning. Proceedings of Parallel Problem Solving from Nature (PPSN 2012), 2012: 216-225
  • O. Regnier-Coudert, J. McCall: An island model genetic algorithm for bayesian network structure learning. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2012), 2012: 1-8
  • Y. Wu, J. McCall, D. Corne, O. Regnier-Coudert: Landscape analysis for hyperheuristic bayesian network structure learning on unseen problems. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2012), 2012: 1-8
  • O. Regnier-Coudert, J. McCall: Privacy-preserving approach to bayesian network structure learning from distributed data. GECCO (Companion) 2011: 815-816
  • A. Brownlee, O. Regnier-Coudert, J. McCall and S. Massie. Using a Markov network as a surrogate fitness function in a genetic algorithm. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2010), 2010: 1-8
  • T. Lam, O. Regnier-Coudert, J. McCall , R. Lothian, S. McClinton. Prostate cancer staging nomograms: Validation on a British population. BJUI 2010;106(s1):2

PhD thesis

  • O. Regnier-Coudert. Bayesian network structure learning using characteristic properties of permutation representations with applications to prostate cancer treatment. Robert Gordon University, 2013. (available at http://hdl.handle.net/10059/834)