School of Computing

Fernando Otero

Lecturer / Outreach Coordinator

Photo of FE Otero, if available
  • Room M3-36
    Medway Building
    Chatham Maritime
    Kent ME4 4AG

Publications

My publications are available from the University of Kent's Academic Repository.

GOOGLE SCHOLAR RESEARCH GATE

Research Interests

I belong to the following research groups:

My main research interests include:

  • Data Mining and Knowledge Discovery, in particular classification and regression – focusing on the creation of interpretable models – and more recently clustering
  • Bio-inspired algorithms, mainly ant colony optimization and genetic programming
  • Application of data mining algorithms in bioinformatics (e.g., protein function prediction) and financial forecasting
  • Large-scale data mining ("Big Data")

I am always happy to discuss research ideas with potential research students and postdocs. In terms of funding, the School of Computing runs an annual PhD scholarship competition. The University has also put together a useful list of funding opportunities for postdoctoral research.

Recent (Selected) Publications

2017
  • MYRA: A Java Ant Colony Optimization Framework for Classification Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO'17 Companion), pp. 1247-1254, 2017. (pdf)
  • Automatic Design of Ant-Miner Mixed Attributes for Classification Rule Discovery. To appear in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'17), pp. 433-440, 2017. (pdf)
  • Evolving trading strategies using directional changes. In: Expert Systems with Applications, Volume 73, pp. 145-160, 2017.
    The final publication is available at http://dx.doi.org/10.1016/j.eswa.2016.12.032
  • Heuristic procedures for improving the predictability of a genetic programming financial forecasting algorithm. In: Soft Computing, Volume 21, pp. 295-310, 2017. (pdf)
2016
  • Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results. In: Evolutionary Computation, Volume 24, Issue 3, pp. 385-409, 2016. (pdf)
2015
  • An ant colony-based semi-supervised approach for learning classification rules. In: Swarm Intelligence, Volume 9, Issue 4, pp. 315-341, 2015. (pdf)
    The final publication is available at Springer via http://dx.doi.org/10.1007/s11721-015-0116-8

Publication Awards and Nominations

  • A Dispersion Operator for Geometric Semantic Genetic Programming. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '16), pp. 773-780, 2016. Received the Best Paper Award in the track "Genetic Programming". (pre-print pdf)
  • The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems. In: Proceedings of the 18th European Conference on Genetic Programming (EuroGP 2015), pp. 3-15, 2015. Nominated for Best Paper Award at EuroGP 2015. (pre-print pdf)
  • Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '13), pp. 73-80, 2013. Received the Best Paper Award in the track "Ant Colony Optimization and Swarm Intelligence". (pre-print pdf)
  • Automated Problem Decomposition for the Boolean Domain with Genetic Programming. In: Proceedings of the 16th European Conference on Genetic Programming (EuroGP 2013), pp. 169-180, 2013. Nominated for Best Paper Award at EuroGP 2013. (pre-print pdf)

Software

Below is a collection of software that I have written during my research:

MYRA is a collection of Ant Colony Optimization (ACO) algorithms for the data mining classification task. It includes popular rule induction and decision tree induction algorithms. The algorithms are ready to be used from the command line or can be easily called from your own Java code. They are build using a modular architecture, so they can be easily extended to incorporate different procedures and/or use different parameter values.

EpochX is an open source genetic programming framework. It is designed specifically for the task of analysing evolutionary automatic programming. Includes full support for 3 popular representations: strongly typed tree GP, context-free grammar GP and grammatical evolution.

Current Research Students

Teaching

I teach on (or am otherwise involved with) the following modules:

Background

I got my PhD in Computer Science from the University of Kent in 2010, doing research on ant colony optimisation (ACO) algorithms for data mining, in particular the creation of ACO algorithms for the bioinformatics problem of predicting protein functions.

School of Computing, University of Kent, Canterbury, Kent, CT2 7NF

Enquiries: +44 (0)1227 824180 or contact us.

Last Updated: 11/12/2017