Dr Mario Andres Munoz Acosta
Associate Investigator, Research Fellow
I received the B.Eng. and M.Eng. degrees in Electronics Engineering from Universidad del Valle, Colombia, in 2005 and 2008, respectively, and the Ph.D. degree in Engineering from The University of Melbourne, in 2014. From 2014 to 2017, I was a Research Fellow at the School of Mathematical Sciences, Monash University. Currently, I am a Research Fellow at the School of Mathematics and Statistics, The University of Melbourne.
My research focuses on the application of optimisation, computational intelligence, signal processing, data analysis, and machine learning methods to ill-defined science, engineering and medicine problems. My current work focuses on the algorithm selection problem for black-box optimisation (both single- and multi-objective) and machine learning problems, anomaly detection on streaming data, minerals criticality, industrial sustainability performance measurement, and gait analysis.
Research Interests:
Complex Systems
Evolutionary computation
Machine Learning
Optimisation
Qualifications:
Bachelor of Engineering (Electronics)
Master of Engineering (Electronics)
Doctor of Philosophy (Engineering)
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
(2018). Does normalizing your data affect outlier detection?.
useR! 2018.
(2017). Generating custom classification datasets by targeting the instance space.
the Genetic and Evolutionary Computation Conference CompanionProceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '17. 1582 - 1588. doi: 10.1145/3067695.3082532
(2017). Non-parametric model of the space of continuous black-box optimization problems.
the Genetic and Evolutionary Computation Conference CompanionProceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '17. 175 - 176. doi: 10.1145/3067695.3075971
(2016). ICARUS: Identification of complementary algorithms by uncovered sets.
2016 IEEE Congress on Evolutionary Computation (CEC)2016 IEEE Congress on Evolutionary Computation (CEC). 2427 - 2432. doi: 10.1109/CEC.2016.7744089
(2015). Effects of function translation and dimensionality reduction on landscape analysis.
2015 IEEE Congress on Evolutionary Computation (CEC)2015 IEEE Congress on Evolutionary Computation (CEC). 1336 - 1342. doi: 10.1109/CEC.2015.7257043
Journal Articles
(2018). Instance spaces for machine learning classification.
Machine Learning. 107(1), 109 - 147. doi: 10.1007/s10994-017-5629-5
(2017). Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space.
Evolutionary Computation. 25(4), 529 - 554. doi: 10.1162/evco_a_00194
(2015). Exploratory Landscape Analysis of Continuous Space Optimization Problems Using Information Content.
IEEE Transactions on Evolutionary Computation. 19(1), 74 - 87. doi: 10.1109/TEVC.2014.2302006
(2015). Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges.
Information Sciences. 317, 224 - 245. doi: 10.1016/j.ins.2015.05.010
(2014). A comparison of optimisation methods and knee joint degrees of freedom on muscle force predictions during single-leg hop landings.
Journal of Biomechanics. 47(12), 2863 - 2868. doi: 10.1016/j.jbiomech.2014.07.027
Technical reports and unrefereed outputs
(2018). Anomaly Detection in Streaming Nonstationary Temporal Data.
(2018). Reliability of Exploratory Landscape Analysis.
(2018). On normalization and algorithm selection for unsupervised outlier detection.
