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

Conference Papers

Year	Citation
2017	Gao, W., Friedrich, T., Kötzing, T. & Neumann, F. (2017). Scaling up local search for minimum vertex cover in large graphs by parallel kernelization. 30th Australasian Joint Conference on Artificial Intelligence (AI). Melbourne, Australia. 10.1007/978-3-319-63004-5_11
2017	Osuna, E., Neumann, F., Gao, W. & Sudholt, D. (2017). Speeding up evolutionary multi-objective optimisation through diversity-based parent selection. The 2017 Genetic and Evolutionary Computation Conference. Berlin, Germany. 10.1145/3071178.3080294
2016	Gao, W., Nallaperuma, S. & Neumann, F. (2016). Feature-based diversity optimization for problem instance classification. 14th International Conference on Parallel Problem Solving from Nature (PPSN 2016). Edinburgh, UK. 10.1007/978-3-319-45823-6_81
2016	Gao, W., Friedrich, T. & Neumann, F. (2016). Fixed-parameter single objective search heuristics for minimum vertex cover. 14th International Conference on Parallel Problem Solving from Nature (PPSN). J. Handl, E. Hart, P. Lewis, M. LopezIbanez, G. Ochoa & B. Paechter (Eds.) Edinburgh, ENGLAND. 10.1007/978-3-319-45823-6_69
2016	Doerr, B., Gao, W. & Neumann, F. (2016). Runtime analysis of evolutionary diversity maximization for OneMinMax. Genetic and Evolutionary Computation Conference (GECCO). Denver, CO. 10.1145/2908812.2908827
2015	Gao, W., Pourhassan, M. & Neumann, F. (2015). Runtime analysis of evolutionary diversity optimization and the vertex cover problem. S. Silva & A. I. Esparcia-Alcázar (Eds.) 10.1145/2739482.2764668
2015	Pourhassan, M., Gao, W. & Neumann, F. (2015). Maintaining 2-approximations for the dynamic vertex cover problem using evolutionary algorithms. 17th Genetic and Evolutionary Computation Conference (GECCO). S. Silva (Ed.) Madrid, SAN MARINO. 10.1145/2739480.2754700
2014	Gao, W. & Neumann, F. (2014). Runtime analysis for maximizing population diversity in single-objective optimization. 16th Genetic and Evolutionary Computation Conference (GECCO). C. Igel (Ed.) Vancouver, CANADA. 10.1145/2576768.2598251