Faculty

GHANDAR, Adam
Assistant Professor
aghandar@sustech.edu.cn

Research Area:
Computational Intelligence, Data Science, Prescriptive Analytics, Decision Systems, Supply Chains and Logistics
Educational Background

◆ PhD, University of Adelaide

◆ B Comp. Sci. (Mathematics) Honours 1, University of New England

Professional Experience

◆ Principal Scientist, Schneider Electric

◆ Post Doctoral Research Fellow, University of Adelaide

Honors & Awards

◆ Post-doc, University of Adelaide

◆ R&D Edison Technical Field Expert, Schneider Electric

Selected Publication

1. Ghandar, A., Theodoropoulos, G., Zheng, B., Chen, S., Gong, Y. and Zhong, M., 2018, October. A Dynamic Data Driven Application System to Manage Urban Agricultural Ecosystems in Smart Cities.In 2018 4th International Conference on Universal Village (UV) (pp. 1-7).IEEE.

2. Ghandar A, Evolutionary Computation to Determine Product Builds in Open Pit Mining, Simulated Evolution and Learning, SEAL2017: 751-762.

3. Ghandar, A., Michalewicz, Z., and Zurbruegg, R. Computer Intelligence Optimization, Model Complexity and Financial Forecasting Performance, International Journal of Forecasting, Volume 32, Issue 3, Pages 585-1102 (July–September 2016)

4. Ghandar A, Michalewicz Z, Zurbruegg R: Enhancing Profitability through Interpretability in Algorithmic Trading with a Multiobjective Evolutionary Fuzzy System. PPSN (2) 2012: 42-51

5. Ghandar A and Michalewicz Z. 2011. Using Cellular Evolution for Diversification of the Balance Between Accurate and Interpretable Fuzzy Knowledge Bases for Classification. IEEE Congress on Evolutionary Computation 2011: 1:8

6. Ghandar A and Michalewicz Z. 2011. Considerations of the Nature of the Relationship between Generalization and Interpretability in Evolutionary Fuzzy Systems.In Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (GECCO '11), NatalioKrasnogor (Ed.).ACM, New York, NY, USA, 97-98.

7. Ghandar A,Michalewicz Z, 2011: An Experimental Study of Multiobjective Evolutionary Algorithms for Balancing Interpretability and Accuracy in Fuzzy Rulebase Classifiers for Financial Prediction. In Compuational Intelligence for Financial Engineering, 2011.CIFEr '11, April 11-15 2011.

8. Ghandar A, Michalewicz Z, Zurbruegg R, Cheong C: Index Tracking Fund Enhancement Using Evolving Multi-Criteria Fuzzy Decision Models. Proceedings WCCI/CEC 2010.

9. Ghandar A, Michalewicz Z, Schmidt M, Tô T, and Zurbrugg R. Computational Intelligence for Evolving Trading Rules. IEEE Trans. Evol. Comp 13, 1 (Feb. 2009), 71-86.

10. Ghandar A, Michalewicz Z, To T, Zurbruegg R: The Performance of an Adaptive Portfolio Management System. IEEE Congress on Evolutionary Computation 2008: 2208-2216.