Evolutionary and Biologically Inspired Computation

Research Group


Current areas of interest

  1. Genetic programming (GP)

  2. -genotype to phenotype mapping methods

  3. -developmental and generative representations

  4. -probabilistic model-building

  5. Applications of GP

  6. Foundations and applications of evolutionary computation

Graduate Students

  1. Jaspreet Kaur Bassan - TBD

  2. Linda Enciu (MSc) - Learning Quantum Programs

  3. Mahsa Mostowfi (MSc) - A Study on Financial Time Series Forecasting and Symbolic Regression by Means of a Hybrid Probabilistic Model-Building Cartesian Genetic Programming Methodology

  4. Mohammad Islam (MSc) - Learning to rank for information retrieval using genetic programming

  5. Sweeney Luis (MSc) - On the Evolvability of A Hybrid Ant Colony-Cartesian Genetic Programming Methodology

  6. Graham Holker (MSc) -  Study on Estimation of Distribution Algorithms for Neuroevolution

  7. Elmira Ghoulbeigi (MSc) - Indirect Estimation of Distribution Algorithms for the Evolution of Tree-Shaped Structures

  8. Stephen Johns (MSc) - A Novel Developmental Genetic Programming Methodology  for  Mathematical Modeling and  Neuroevolution

  9. Nigel Browne (MSc) - Adaptive Representations for Improving Evolvability, Parameter tuning, and Parallelization of Gene Expression Programming

  10. Fereshteh Mahvarsayad (MEng) - Texture Classification Using Gene Expression Programming

  11. Mauro Gazzani (PhD, co-superv.) - Error-free Programming in a Distributed Execution Programming Language

  12. Ji Wen Ge (MASc) - Concurrent Transaction Logic with Priority and Timing Constraints



To advance the capabilities of evolutionary computation by enhancing its computational, representational, and evolutionary methods.

To deploy these advancements in real complex systems applications, to learn how they can be used to enhance the understanding of those systems.