ORCID Profile
0000-0002-1716-690X
Current Organisation
Monash University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Software Engineering | Stellar Astronomy and Planetary Systems | Astronomical and Space Sciences | Neural, Evolutionary and Fuzzy Computation | Pattern Recognition and Data Mining | Galactic Astronomy | Computer Software | Artificial Intelligence and Image Processing
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in the Physical Sciences | Expanding Knowledge in the Information and Computing Sciences | Computer Software and Services not elsewhere classified | Application Tools and System Utilities |
Related Links
Publications
Software Designers in Action
Publisher: Chapman and Hall/CRC
Date: 10-09-2013
DOI: 10.1201/B15530
Feature-based software design pattern detection
Publisher: Elsevier BV
Date: 03-2022
Optimizing the sum of maximum earliness and tardiness of the job shop scheduling problem
Publisher: Elsevier BV
Date: 05-2017
Identifying Features of Fitness Landscapes and Relating Them to Problem Difficulty
Publisher: MIT Press - Journals
Date: 09-2017
DOI: 10.1162/EVCO_A_00177
Abstract: Complex combinatorial problems are most often optimised with heuristic solvers, which usually deliver acceptable results without any indication of the quality obtained. Recently, predictive diagnostic optimisation was proposed as a means of characterising the fitness landscape while optimising a combinatorial problem. The scalars produced by predictive diagnostic optimisation appear to describe the difficulty of the problem with relative reliability. In this study, we record more scalars that may be helpful in determining problem difficulty during the optimisation process and analyse these in combination with other well-known landscape descriptors by using exploratory factor analysis on four landscapes that arise from different search operators, applied to a varied set of quadratic assignment problem instances. Factors are designed to capture properties by combining the collinear variances of several variables. The extracted factors can be interpreted as the features of landscapes detected by the variables, but disappoint in their weak correlations with the result quality achieved by the optimiser, which we regard as the most reliable indicator of difficulty available. It appears that only the prediction error of predictive diagnostic optimisation has a strong correlation with the quality of the results produced, followed by a medium correlation of the fitness distance correlation of the local optima.
Adaptive virtual machine migration mechanism for energy efficiency
Publisher: ACM
Date: 14-05-2016
Mapping the Effectiveness of Automated Test Suite Generation Techniques
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Predictive parameter control
Publisher: ACM
Date: 12-07-2011
Instance Space Analysis of Search-Based Software Testing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
An Experimental Assessment of Using Theoretical Defect Predictors to Guide Search-Based Software Testing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Adaptive data transfer methods via policy evolution for UAV swarms
Publisher: IEEE
Date: 11-2017
Evaluating probabilistic models with uncertain model parameters
Publisher: Springer Science and Business Media LLC
Date: 09-2012
Quality optimisation of software architectures and design specifications
Publisher: Elsevier BV
Date: 10-2013
Architecture-Driven Reliability and Energy Optimization for Complex Embedded Systems
Publisher: Springer Berlin Heidelberg
Date: 2010
Robust ArcheOpterix: Architecture optimization of embedded systems under uncertainty
Publisher: IEEE
Date: 06-2012
Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control
Publisher: Elsevier BV
Date: 12-2021
VisArch: Visualisation of Performance-based Architectural Refactorings
Publisher: Springer International Publishing
Date: 2020
Adaptive Range Parameter Control
Publisher: IEEE
Date: 06-2012
Studying feedback mechanisms for adaptive parameter control in evolutionary algorithms
Publisher: IEEE
Date: 06-2013
Collective Hyper-heuristics for Self-assembling Robot Behaviours
Publisher: Springer International Publishing
Date: 2018
A mixed integer linear programming model for reliability optimisation in the component deployment problem
Publisher: Informa UK Limited
Date: 08-2016
An iterated local search with guided perturbation for the heterogeneous fleet vehicle routing problem with time windows and three-dimensional loading constraints
Publisher: Springer International Publishing
Date: 27-12-2017
Age pensioner decumulation: Responses to incentives, uncertainty and family need
Publisher: SAGE Publications
Date: 03-05-2017
Abstract: Effective design and regulation of retirement benefits require accurate understanding of how the elderly decumulate. We analyse the income, assets and decumulation patterns of a longitudinal panel of 10,000 Australian age pensioners. On average, age pensioners preserve financial and residential wealth and leave substantial bequests. There is, however, considerable heterogeneity in decumulation patterns. Younger households generally run down financial wealth, while older households maintain their assets or save. Means-testing accelerates decumulation, with average drawdown rates 3% higher for pensioners subject to the income test relative to full pensioners and 9% higher for those subject to the asset test relative to full pensioners. Loss of a partner is linked to large falls in assets. The theoretical, empirical, and practical implications of these findings are discussed.
An adaptive memetic algorithm for the architecture optimisation problem
Publisher: Springer International Publishing
Date: 27-12-2017
Let the Ants Deploy Your Software - An ACO Based Deployment Optimisation Strategy
Publisher: IEEE
Date: 11-2009
DOI: 10.1109/ASE.2009.59
Ontological and Computational Aspects of Economic-Environmental Modelling
Publisher: Springer International Publishing
Date: 2015
A systematic literature review of adaptive parameter control methods for evolutionary algorithms
Publisher: Association for Computing Machinery (ACM)
Date: 21-10-2016
DOI: 10.1145/2996355
Abstract: Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide range of problems. Their robustness, however, may be affected by several adjustable parameters, such as mutation rate, crossover rate, and population size. Algorithm parameters are usually problem-specific, and often have to be tuned not only to the problem but even the problem instance at hand to achieve ideal performance. In addition, research has shown that different parameter values may be optimal at different stages of the optimisation process. To address these issues, researchers have shifted their focus to adaptive parameter control, in which parameter values are adjusted during the optimisation process based on the performance of the algorithm. These methods redefine parameter values repeatedly based on implicit or explicit rules that decide how to make the best use of feedback from the optimisation algorithm. In this survey, we systematically investigate the state of the art in adaptive parameter control. The approaches are classified using a new conceptual model that sub ides the process of adapting parameter values into four steps that are present explicitly or implicitly in all existing approaches that tune parameters dynamically during the optimisation process. The analysis reveals the major focus areas of adaptive parameter control research as well as gaps and potential directions for further development in this area.
Constraint Programming and Ant Colony System for the Component Deployment Problem
Publisher: Elsevier BV
Date: 2014
Quality assessment of multiobjective optimisation algorithms in component deployment
Publisher: ACM
Date: 25-08-2009
The nature of nature
Publisher: Springer International Publishing
Date: 2017
Entropy-based adaptive range parameter control for evolutionary algorithms
Publisher: ACM
Date: 06-07-2013
Architecture-based reliability evaluation under uncertainty
Publisher: ACM
Date: 07-11-2023
Hyper-heuristic Online Learning for Self-assembling Swarm Robots
Publisher: Springer International Publishing
Date: 2018
An efficient method for uncertainty propagation in robust software performance estimation
Publisher: Elsevier BV
Date: 04-2018
Can Monte-Carlo Tree Search learn to sacrifice?
Publisher: Springer Science and Business Media LLC
Date: 13-10-2016
Choosing the Appropriate Forecasting Model for Predictive Parameter Control
Publisher: MIT Press - Journals
Date: 06-2014
DOI: 10.1162/EVCO_A_00113
Abstract: All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.
Designing and characterising fitness landscapes with various operators
Publisher: IEEE
Date: 06-2013
Investigating the correlation between indicators of predictive diagnostic optimisation and search result quality
Publisher: Elsevier BV
Date: 12-2016
Adaptive Neighbourhood Search for the Component Deployment Problem
Publisher: Springer International Publishing
Date: 2015
Designing automotive embedded systems with adaptive genetic algorithms
Publisher: Springer Science and Business Media LLC
Date: 23-04-2015
VisArch: Visualization of Performance-based Architectural Refactorings
Publisher: Zenodo
Date: 2020
VisArch: Visualization of Performance-based Architectural Refactorings
Publisher: Zenodo
Date: 2020
VisArch: Visualization of Performance-based Architectural Refactorings
Publisher: Zenodo
Date: 2020
Software Architecture Optimization Methods: A Systematic Literature Review
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2013
DOI: 10.1109/TSE.2012.64
VisArch: Visualization of Performance-based Architectural Refactorings
Publisher: Zenodo
Date: 2020
Characterising fitness landscapes using predictive local search
Publisher: ACM
Date: 06-07-2013
A Genome-Wide Association Meta-Analysis of Circulating Sex Hormone–Binding Globulin Reveals Multiple Loci Implicated in Sex Steroid Hormone Regulation
Publisher: Public Library of Science (PLoS)
Date: 19-07-2012
Model-based performance analysis of software architectures under uncertainty
Publisher: ACM
Date: 17-06-2013
Architecture-driven reliability optimization with uncertain model parameters
Publisher: Elsevier BV
Date: 10-2012
Distributing Fibre Boards: A Practical Application of the Heterogeneous Fleet Vehicle Routing Problem with Time Windows and Three-dimensional Loading Constraints
Publisher: Elsevier BV
Date: 2015
Efficient Sensitivity Analysis of Reliability in Embedded Software
Publisher: Springer International Publishing
Date: 2015
Fitness landscape characterisation for constrained software architecture optimisation problems
Publisher: IEEE
Date: 12-2016
Test data generation with a Kalman filter-based adaptive genetic algorithm
Publisher: Elsevier BV
Date: 05-2015
What makes software design effective?
Publisher: Elsevier BV
Date: 11-2010
Analysing the fitness landscape of search-based software testing problems
Publisher: Springer Science and Business Media LLC
Date: 23-03-2017
Reliability-driven deployment optimization for embedded systems
Publisher: Elsevier BV
Date: 05-2011
ArcheOpterix: An extendable tool for architecture optimization of AADL models
Publisher: IEEE
Date: 05-2009
Component deployment optimisation with bayesian learning
Publisher: ACM
Date: 20-06-2011
Energy-efficient adaptive virtual machine migration mechanism for private Clouds
Publisher: Wiley
Date: 15-05-2017
DOI: 10.1002/CPE.4170
ABO blood group and risk of epithelial ovarian cancer within the Ovarian Cancer Association Consortium
Publisher: Springer Science and Business Media LLC
Date: 08-09-2012
Related Organisations
Related Funding Activities
Discovery Early Career Researcher Award - Grant ID: DE140100017
Start Date: 01-2014
End Date: 12-2019
Amount: $394,800.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP160100637
Start Date: 05-2016
End Date: 08-2019
Amount: $344,100.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP210100041
Start Date: 09-2021
End Date: 09-2024
Amount: $420,000.00
Funder: Australian Research Council
View Funded Activity