
Dr Wanru Gao
Lecturer
School of Computer Science
Faculty of Engineering, Computer and Mathematical Sciences
I'm currently a lecturer in the Optimisation and Logistic group in the School of Computer Science of University of Adelaide. My research interests include:
Algorithm Analysis
Genetic and Evolutionary Algorithms
Multi-objective Optimization
Combinatorial Optimization
Research Interests
- Algorithm Analysis
- Genetic and Evolutionary Algorithms
- Multi-objective Optimization
- Combinatorial Optimization
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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.
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.
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).
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.
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.
Scopus32017 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.
Scopus12017 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).
Scopus42016 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.
Scopus8 WoS12016 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.
Scopus4 WoS12016 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.
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.
Scopus9 WoS42015 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.
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.
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
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