Optimisation and Logistics at the University of AdelaideWe research artificial intelligence and optimisation methods that are frequently used to solve hard and complex optimization problems. These include linear programming, branch and bound, genetic algorithms, evolution strategies, genetic programming, ant colony optimization, particle swarm optimization, local search, and other related approaches.
In our research related to real-world applications, we pay particular attention to constraint-handling techniques, multi-objectivity, and dynamic environments. These aspects are always present in large-scale industrial problems, in particular, they are important in integrated planning and scheduling decision-support systems which relate to supply-chain operations. Furthermore, we focus on the development of new algorithms for applications in the area of renewable energy and the use of artificial intelligence and optimisation methods in the area of software engineering.
In our theoretical research we analyze how bio-inspired computing methods and other search methods from the area of artificial intelligences work and show in a rigorous way how they are able to deal with different types of problems. Our theoretical research builds up a theory of bio-inspired computing and related search techniques that helps to develop new effective approaches based on theoretical insights. We also investigate problems in the areas of mechanism design and social choice and develop new approaches for dealing with game theoretic problems. Furthermore, we use artificial intelligence for the creation of digital art.
PPSN 2018The International Conference on Parallel Problem Solving from Nature (PPSN) is a leading conference on nature-inspired computing which takes place every 2 years in Europe. We and our international collaborators have the following 6 full papers accepted at PPSN 2018:
- B. Ghasemishabankareh, M. Ozlen, F. Neumann, X. Li: A Probabilistic Tree-Based Representation for Non-convex Minimum Cost Flow Problems.
- T. Friedrich, A. Göbel, F. Quinzan, M. Wagner: Heavy-tailed Mutation Operators in Single-Objective Combinatorial Optimization. Preliminary version
- V. Roostapour, A. Neumann, F. Neumann: On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem. Preliminary version
- F. Neumann, A. M. Sutton: Runtime Analysis of Evolutionary Algorithms for the Knapsack Problem with Favorably Correlated Weights. Preliminary version
- C. Doerr, M. Wagner: Sensitivity of Parameter Control Mechanisms with Respect to Their Initialization. Preliminary version
- D. R. Arbones, N. Y. Sergiienko, B. Ding, O. Krause, C. Igel, M. Wagner: Sparse incomplete LU-decomposition for Wave Farm Designs under Realistic Conditions. Preliminary version
GECCO 2018The ACM Genetic and Evolutionary Computation Conference (GECCO) is the premier conference for research in the area of evolutionary computation. Each year the best evolutionary computation research outcomes are presented at this prestigious conference. We and our international collaborators have the following 8 full papers accepted at GECCO 2018:
- Wu, Polyakovskiy, Wagner, F. Neumann: Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem. Arxiv version
- A. Neumann, Gao, Doerr, F. Neumann, Wagner: Discrepancy-based Evolutionary Diversity Optimization. Arxiv version
- Neshat, Alexander, Wagner, Xia: A Detailed Comparison of Meta-Heuristic Methods for Optimising Wave Energy Converter Placements.
- Friedrich, Quinzan, Wagner: Escaping Large Deceptive Basins of Attraction with Heavy Mutation Operators.
- El Yafrani, Martins, Krari, Wagner, Delgado, Ahiod, Lüders: A fitness landscape analysis of the Travelling Thief Problem.
- Doerr, Wagner: Simple On-the-Fly Parameter Selection Mechanisms for Classical Discrete Black-Box Optimization Benchmarks. Arxiv version
- Shi, F. Neumann, Wang: Runtime Analysis of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem. Preliminary version
- Gao, Friedrich, F. Neumann, Hercher: Randomized Greedy Algorithms for Covering Problems. Preliminary version
Research ConsortiumOur group is a major investigator of the $14.6 million Research Consortium – Unlocking Complex Resources through Lean Processing led by the University of Adelaide and funded through the Research Consortia Program of the State Government of South Australia, 2017-2021. The other Consortium industry, government and supporting partners are: BHP, OZ Minerals, AMIRA International, Australian Information Industries Association (AIIA) IoT Cluster for Mining and Energy Resources, Australian Semi-Conductor Technology Company, Boart Longyear, Consilium Technology, CRC Optimising Resource Extraction, Datanet, Data to Decisions CRC, Eka, Innovyz, Magotteaux, Manta Controls, Maptek, METS Ignited Industry Growth Centre, Mine Vision Systems, Rockwell Automation, SACOME, SAGE Automation, Sandvik, Scantech, South Australian Mining Industry Participation Office (SA MIPO), SRA IT and Thermo Fisher Scientific Australia (Processing Instruments & Equipment), with the University of South Australia as a key research partner.
- Algorithmic Game Theory
- Combinatorial Optimisation and Logistics
- Foundations of Bio-Inspired Computing
- Renewable Energy
- Search-based Software Engineering