Footprints in instance space: visualising the suitability of optimisation algorithms. Optimisation problems underpin the efficiency and effectiveness of many critical sectors (e.g., healthcare, manufacturing, and defence). The project will provide both practitioners and researchers with powerful new tools to develop a much more robust understanding of the strengths and weaknesses of a variety of optimisation algorithms.
Australian Laureate Fellowships - Grant ID: FL140100012
Funder
Australian Research Council
Funding Amount
$2,830,000.00
Summary
Stress-testing algorithms: generating new test instances to elicit insights. Stress-testing algorithms: generating new test instances to elicit insights. This project aims to develop a new paradigm in algorithm testing, creating novel test instances and tools to elicit insights into algorithm strengths and weaknesses. Such advances are urgently needed to support good research practice in academia, and to avoid disasters when deploying algorithms in practice. Extending our recent work in algorith ....Stress-testing algorithms: generating new test instances to elicit insights. Stress-testing algorithms: generating new test instances to elicit insights. This project aims to develop a new paradigm in algorithm testing, creating novel test instances and tools to elicit insights into algorithm strengths and weaknesses. Such advances are urgently needed to support good research practice in academia, and to avoid disasters when deploying algorithms in practice. Extending our recent work in algorithm testing for combinatorial optimisation, described as 'ground-breaking,' this project aims to tackle the challenges needed to generalise the paradigm to other fields such as machine learning, forecasting, software testing, and other branches of optimisation. An online repository of test instances and tools aim to provide a valuable resource to improve research practice and support new insights into algorithm performance.Read moreRead less
Pattern Recognition and Interpretation in Sequence Data. With the recent advances in sequencing technology, the amount of biological sequence data available has increased tremendously. Extraction of knowledge from such data has lagged behind, awaiting the development of new automated methods for extracting meaning from the sequences. This project aims to develop fast and flexible algorithms for discovery of patterns in DNA and protein sequence data and to find families of sequences that share si ....Pattern Recognition and Interpretation in Sequence Data. With the recent advances in sequencing technology, the amount of biological sequence data available has increased tremendously. Extraction of knowledge from such data has lagged behind, awaiting the development of new automated methods for extracting meaning from the sequences. This project aims to develop fast and flexible algorithms for discovery of patterns in DNA and protein sequence data and to find families of sequences that share similar patterns. Association of these patterns with features of 3-dimensional structures of protein families and their functional characteristics can contribute towards the understanding of the relationship between primary structure and function of a protein.Read moreRead less