Dr Wanru Gao

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• Education

  Date	Institution name	Country	Title
  —	University of Adelaide, Adelaide	Australia	PhD

• Postgraduate Training

  Date	Title	Institution	Country
  —	Bachelor of Software Engineering	University of adelaide	Australia

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• Journals

  Year	Citation
  2018	Covantes Osuna, E., Gao, W., Neumann, F., & Sudholt, D. (2018). Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation. Theoretical Computer Science, Online, 1-20. DOI
  2018	Neumann, A., Gao, W., Doerr, C., Neumann, F., & Wagner, M. (2018). Discrepancy-based Evolutionary Diversity Optimization.. CoRR, abs/1802.05448.

• Conference Papers

  Year	Citation
  2019	Neumann, A., Gao, W., Wagner, M., & Neumann, F. (2019). Evolutionary Diversity Optimization Using Multi-Objective Indicators. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference (pp. 837-845). Prague, Czech Republic. DOI
  2019	Gao, W., Pourhassan, M., Roostapour, V., & Neumann, F. (2019). Runtime analysis of evolutionary multi-objective algorithms optimising the degree and diameter of spanning trees. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11411 LNCS (pp. 504-515). DOI
  2019	Neumann, A., Gao, W., Wagner, M., & Neumann, F. (2019). Evolutionary Diversity Optimization Using Multi-Objective Indicators. In https://gecco-2019.sigevo.org/index.html/HomePage (pp. 1-9). online: ACM.
  2018	Gao, W., Neumann, F., Friedrich, T., & Hercher, C. (2018). Randomized greedy algorithms for covering problems. In H. E. Aguirre, & K. Takadama (Eds.), GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 309-315). Kyoto: ACM. DOI
  2018	Neumann, A., Gao, W., Doerr, C., Neumann, F., & Wagner, M. (2018). Discrepancy-based evolutionary diversity optimization. In H. E. Aguirre, & K. Takadama (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018) (pp. 991-998). Kyoto, JAPAN: Association for Computing Machinery. DOI Scopus3
  2017	Gao, W., Friedrich, T., Kötzing, T., & Neumann, F. (2017). Scaling up local search for minimum vertex cover in large graphs by parallel kernelization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10400 LNAI (pp. 131-143). Melbourne, Australia: Springer Nature. DOI Scopus1
  2017	Osuna, E., Neumann, F., Gao, W., & Sudholt, D. (2017). Speeding up evolutionary multi-objective optimisation through diversity-based parent selection. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 553-560). Berlin, Germany: Association for Computing Machinery (ACM). DOI Scopus4
  2016	Gao, W., Nallaperuma, S., & Neumann, F. (2016). Feature-Based Diversity Optimization for Problem Instance Classification.. In J. Handl, E. Hart, P. R. Lewis, M. López-Ibáñez, G. Ochoa, & B. Paechter (Eds.), PPSN Vol. 9921 (pp. 869-879). Springer.
  2016	Gao, W., Nallaperuma, S., & Neumann, F. (2016). Feature-based diversity optimization for problem instance classification. In Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN 2016) Vol. 9921 LNCS (pp. 869-879). Edinburgh, UK: Springer International Publishing. DOI Scopus8 WoS1
  2016	Doerr, B., Gao, W., & Neumann, F. (2016). Runtime analysis of evolutionary diversity maximization for OneMinMax. In Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 557-564). Denver, Colorado, USA: Association for Computing Machinery. DOI Scopus4 WoS1
  2016	Gao, W., Friedrich, T., & Neumann, F. (2016). Fixed-parameter single objective search heuristics for minimum vertex cover. In J. Handl, E. Hart, P. Lewis, M. Lopez-Ibanez, G. Ochoa, & B. Paechter (Eds.), Proceedings of the 14th International Conference on Parallel Problem Solving from Nature Vol. 9921 LNCS (pp. 740-750). Edinburgh, UK: Springer. DOI
  2015	Pourhassan, M., Gao, W., & Neumann, F. (2015). Maintaining 2-approximations for the dynamic vertex cover problem using evolutionary algorithms. In S. Silva (Ed.), Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 903-910). Madrid, Spain: Association for Computing Machinery. DOI Scopus9 WoS4
  2015	Gao, W., Pourhassan, M., & Neumann, F. (2015). Runtime analysis of evolutionary diversity optimization and the vertex cover problem. In S. Silva, & A. Esparcia-Alcázar (Eds.), Proceedings of the Companion Publication of the 2015 Genetic and Evolutionary Computation Conference (pp. 1395-1396). Madrid, Spain: ACM. DOI
  2014	Gao, W., & Neumann, F. (2014). Runtime analysis for maximizing population diversity in single-objective optimization. In C. Igel (Ed.), Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (pp. 777-784). Vancouver, Canada: Association for Computing Machinery. DOI Scopus8 WoS3

Introduction to Programming, Course Coordinator, Semester 1, 2019.

Algorithm Design & Data Structure, Course Coordinator, Semester 2, 2017.

Object Oriented Programming, Course Coordinator, Semester 2, 2017.

Algorithm Design & Data Structure, Course Coordinator, Semester 1, 2017.

Advanced Algorithms, Lecturer , Semester 1, 2015.

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• Current Higher Degree by Research Supervision (University of Adelaide)

  Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
  2017	Co-Supervisor	Bio-Inspired computing for problems with Dynamically changing constraints	Doctor of Philosophy	Doctorate	Full Time	Mr Vahid Roostapour
  2017	Co-Supervisor	Optimization of Energy Systems with Dynamic Constraints	Doctor of Philosophy	Doctorate	Full Time	Mrs Maryam Hasani Shoreh

• Position: Lecturer
• Phone: 83137611
• Email: wanru.gao@adelaide.edu.au
• Fax: 8313 4366
• Campus: North Terrace
• Building: Ingkarni Wardli, floor 4
• Room: 4 27
• Org Unit: School of Computer Science