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Sheridan Houghten

Sheridan Houghten
Sheridan Houghten
Office: J313
(905) 688-5550 ext. 4526
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Research Interests
Education
Recent Publications
Recent M.Sc. Student Thesis
Current M.Sc. Student Thesis

Research Interests

My research interests include combinatorial optimization, algorithms (in the general sense), and computational intelligence. My research is directed at various application areas, including bioinformatics, coding theory and more.

Prospective Students: If you are a student considering completing a Master's degree or an Honours project and you are interested in any of the above topics, then please contact me. Students should be motivated and comfortable with mathematics (primarily discrete mathematics). Since it can be very difficult to evaluate applications, you should provide as much information as possible about your interests - for example, you might describe projects which you have already undertaken or in which you are interested.

I work closely with my students and ensure that all research students will have the opportunity to publish their work in journals or conference proceedings. You should also look at the list of projects completed by some of my past and present students. See my personal webpage for more information.


Education


Recent Publications

  • D. Ashlock and S. Houghten, Hybridization and Ring Optimization for Larger Sets of Embeddable Biomarkers, 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, accepted (2017).
  • T.K. Collins, A. Zakirov, J.A. Brown and S. Houghten, Single-Objective and Multi-Objective Genetic Algorithms for Compression of Biological Networks, 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, accepted (2017).
  • J. Orth, S. Houghten and L. Tulloch, Evaluation of the Salmon Algorithm, 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, accepted (2017).
  • T. Ribaric and S. Houghten, Genetic Programming for Improved Cryptanalysis of Elliptic Curve Cryptosystems, 2017 IEEE Congress on Evolutionary Computation, accepted (2017).
  • P.E. Becker, M. Derka, S. Houghten and J. Ulrich, Build a Sporadic Group in Your Basement, American Mathematical Monthly 124, p. 291-301, 2017.
  • J.A. Hughes, S. Houghten and D. Ashlock, Permutation Problems, Genetic Algorithms, and Dynamic Representations, book chapter for “Nature Inspired Computing and Optimization: Theory and Applications”, Springer book series on Modelling and Optimization in Science and Technology, p.123-149, 2017.
  • J.A. Hughes, S. Houghten and D. Ashlock, Restarting and recentering genetic algorithm variations for DNA fragment assembly: The necessity of a multi-strategy approach, BioSystems 150, p.35-45, 2016.
  • J.A. Brown, S. Houghten, T. Kennedy Collins and Q. Qu, Evolving Graph Compression using Similarity Measures for Bioinformatics Applications, 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 1-6, 2016.
  • A. Entezari Heravi and S. Houghten, A Methodology for Disease Gene Association using Centrality Measures, 2016 IEEE World Congress on Computational Intelligence, 24-31, 2016.
  • L. Plant and S. Houghten, Properties of Optimal and Near Optimal Edit Metric Error Correcting Codes, Congressus Numerantium 224, p.147-157, 2015.
  • D. Ashlock and S. Houghten, Lexicode Crossover for Embeddable Biomarkers, 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 1-7, 2015.
  • A. Entezari Heravi, K. Tahmasebipour and S. Houghten, Evolutionary Computation for Disease Gene Association, 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 1-8, 2015.
  • M. Goodarzi, S. Houghten and P. Liang, Effect of Multi-K Contig Merging in de novo DNA Assembly, 2014 IEEE Conference on Bioinformatics and Bioengineering, 355-361, 2014.
  • K. Tahmasebipour and S. Houghten, Disease-Gene Association using a Genetic Algorithm, 2014 IEEE Conference on Bioinformatics and Bioengineering, 191-197, 2014.
  • J. Hughes, S. Houghten and D. Ashlock, Recentering and Restarting a Genetic Algorithm using a Generative Representation for an Ordered Gene Problem, International Journal of Hybrid Intelligent Systems, Vol.11, No.4, 257-271, 2014.
  • C.Price, S.Houghten, S.Vassiliev and D.Bruce, Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Evolutionary Algorithms, 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2014.
  • J.Hughes, S.Houghten, G.Mallen-Fullerton and D.Ashlock, Recentering and Restarting Genetic Algorithm Variations for DNA Fragment Assembly, 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2014.
  • G. Mallen-Fullerton, J.Hughes, S.Houghten and G. Fernandez-Anaya, Benchmark Data Sets for the DNA Fragment Assembly Problem, International Journal of Bio-Inspired Computation, Vol.5, No.6, p.384-394, 2013.
  • J.Hughes, S.Houghten and D.Ashlock, Recentering, Reanchoring and Restarting an Evolutionary Algorithm, 5th World Congress on Nature and Biologically Inspired Computing, p.76-83, 2013.
  • J.Hughes, J.Brown, S.Houghten and D.Ashlock, Edit Metric Decoding: Representation Strikes Back, IEEE Congress on Evolutionary Computation, p.229-236, 2013.
  • M.Derka, S.Houghten and P.Becker, A methodology for constructing the basis of a putative (72,36,16) extremal code for a given automorphism group, Congressus Numerantium 212, p.173-193, 2012.
  • Z.Li and S.Houghten, Searching for optimal deletion correcting codes: new properties and extensions of Tenengolts Codes, 12th IEEE International Conference on Computer and Information Technology (CIT 2012), p. 647-654, 2012.
  • D.Ashlock, S.Houghten, J.A.Brown and and J.Orth, "On the Synthesis of DNA Error Correcting Codes", BioSystems 110, p.1-8, 2012.
  • D.E.McCarney, S.Houghten and B.J.Ross, Evolutionary Approaches to the Generation of Optimal Error Correcting Codes, Genetic and Evolutionary Computation Conference (GECCO 2012).
  • F.Alizadeh Noori and S.Houghten, A MultiObjective Algorithm with Side Effect Machines for Motif Discovery, 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Recent M.Sc. Graduate Student Thesis

  • Veronica Suaste Morales, Lossy Compression of Quality Values in Next-Generation Sequencing Data, 2017
  • Ashkan Entezari, Gene Prioritization using Genetic Programming, 2015
  • Koosha Tahmasebipour, Disease-Gene Association using a Genetic Algorithm, 2014
  • James Hughes, Ordered Gene Problems including DNA Fragment Assembly, 2014
  • Collin Price, Modeling Protein Complexes using Genetic Algorithms, 2014
  • Mohammad Goodarzi, Algorithms for De Novo Assembly of Short DNA Reads, 2014
  • Farhad Alizadeh Noori, Motif Discovery, 2012
  • Martin Derka, Self-Dual Codes, 2012
  • John Orth, The Salmon Algorithm - A New Population Based Search Metaheuristic, 2012
  • Zhiyuan Li, Construction of 1-Deletion-Correcting Ternary Codes, 2011
  • Pascal Comte, Bio-Inspired Optimization & Sampling Technique for Side-chain Packing in MCCE, 2010
  • Jing Sun, Bounds on Edit Metric Codes with Combinatorial DNA Constraints, 2009
  • Joseph Brown, Decoding Algorithms using Side Effect Machines, 2009

Current M.Sc. Graduate Student Thesis

  • Tyler Kennedy Collins, in progress
  • Yaroslava Girilishena, Complete Sequence Charactization of Structural Variants in the Human Genome
  • Yosra Kazemi, Prediction of Stages of Alzheimer's Disease using FMRI Data
  • Tim Ribaric, Elliptic Curve Cryptography using Computational Intelligence