20152019

Research output per year

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Personal profile

Biography

Simon completed his PhD in 2019, focusing on generating test cases that effectively stress-test optimisation algorithms and determine their relative strengths and weaknesses. He has a particular interest in optimisation algorithm development, empirical performance analysis, and automated algorithm configuration using machine learning methods.

Simon also has extensive experience applying optimisation and data analysis techniques to real-world problems, including optimisation for road and rail logistics, development of automated tools to analyse operational data from wind farms; and short-term energy production forecasting.

Education/Academic qualification

Mathematics, Doctorate of Philosophy - Science, University of Melbourne

9 Mar 201512 Dec 2019

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Research Output

  • 2 Article
  • 1 Conference Paper

A genetic algorithm hybridisation scheme for effective use of parallel workers

Bowly, S., 13 Jul 2019, GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference: 2019 Genetic and Evolutionary Computation Conference, GECCO 2019; Prague; Czech Republic; 13 July 2019 through 17 July 2019. New York NY USA: Association for Computing Machinery (ACM), p. 745-752 8 p. (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

Open Access
File

Generation techniques for linear programming instances with controllable properties

Bowly, S., Smith-Miles, K., Baatar, D. & Mittelmann, H., 1 Jan 2019, (Accepted/In press) In : Mathematical Programming Computation. 27 p.

Research output: Contribution to journalArticleResearchpeer-review

Open Access

Generating new test instances by evolving in instance space

Smith-Miles, K. A. & Bowly, S., 2015, In : Computers and Operations Research. 63, p. 102-113 12 p.

Research output: Contribution to journalArticleResearchpeer-review

20 Citations (Scopus)