Further Enquiries

School of Computer and Mathematical Sciences
Ingkarni Wardli Building
The University of Adelaide
SA 5005
AUSTRALIA
Email

Telephone: +61 8 8313 5586
Facsimile: +61 8 8313 4366

Optimisation and Logistics at the University of Adelaide

We 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.

News

PPSN 2024

The 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 9 full papers accepted at PPSN 2024:
  • D. Antipov, A. Neumann, F. Neumann, A. M. Sutton: Runtime analysis of evolutionary diversity optimization on the multi-objective (LeadingOnes, TrailingZeros) problem.
  • X. Yan, A. Neumann, F. Neumann: Sliding window bi-objective evolutionary algorithms for optimizing chance-constrained monotone submodular functions.
  • A. V. Do, M. Guo, A. Neumann, F. Neumann: Evolutionary multi-objective diversity optimization.
  • F. Neumann, C. Witt: Sliding window 3-objective Pareto optimization for problems with chance constraints.
  • J. G. Harder, A. Neumann, F. Neumann: Analysis of evolutionary diversity optimisation for the maximum matching problem.
  • K. Perera, F. Neumann, A. Neumann: Multi-objective evolutionary approaches for the knapsack problem with stochastic profits.
  • F. Neumann, G. Rudolph: Archive-based single-objective evolutionary algorithms for submodular optimization.
  • D. Antipov, A. Neumann, F. Neumann: Local optima in diversity optimization: non-trivial offspring population is essential.
  • D. Antipov, T. Kötzing, A. Radhakrishnan: Greedy versus curious parent selection for multi-objective evolutionary algorithms.

GECCO 2024

The ACM Genetic and Evolutionary Computation Conference (GECCO) is the internationally leading conference on evolutionary computation and GECCO 2024 will take place in Melbourne from 14-18 July 2024. This is the first time that the conference is held in Australia. The Optimisation and Logistics group together with their national and international collaborators has 15 full papers on a wide range of fundamental topics and applications accepted at this prestigious conference.
  • T. C. Pathirage Don, A. Neumann, F. Neumann: The chance constrained travelling thief problem: problem formulations and algorithms.
  • I. Hewa Pathiranage, F. Neumann, D. Antipov, A. Neumann: Effective 2- and 3-objective MOEA/D approaches for the chance constrained knapsack problem.
  • I. Hewa Pathiranage, F. Neumann, D. Antipov, A. Neumann: Using 3-objective evolutionary algorithms for the dynamic chance constrained knapsack problem.
  • F. Ye, F. Neumann, J. de Nobel, A. Neumann, T. Bäck: What performance indicators to use for self-adaptation in multi-objective evolutionary algorithms.
  • D. Antipov, A. Neumann, F. Neumann: A detailed experimental analysis of evolutionary diversity optimization for OneMinMax.
  • A. Nikfarjam, T. Stanford, A. Neumann, D. Dumuid, F. Neumann: Quality diversity approaches for time-use optimisation to improve health outcomes. (Best Paper Award in the track "Real World Applications")
  • M. Schmidbauer, A. Opris, J. Bossek, F. Neumann, D. Sudholt: Guiding quality diversity on monotone submodular functions: customising the feature space by adding Boolean conjunctions.
  • B. Doerr, J. Knowles, A. Neumann, F. Neumann: A block-coordinate descent EMO algorithm: theoretical and empirical analysis.
  • X. Yan, A. Neumann, F. Neumann: Sampling-based Pareto optimization for chance-constrained monotone submodular problems.
  • S. Sadeghi Ahouei, J. de Nobel, A. Neumann, T. Bäck, F. Neumann: Evolving reliable differentiating constraints for the chance-constrained maximum coverage problem.
  • S. Gounder, F. Neumann, A. Neumann: Evolutionary diversity optimisation for sparse directed communication networks.
  • A. Opris, D. Dang, F. Neumann, Sudholt: Runtime analyses of NSGA-III on many-objective problems.
  • D. Antipov, B. Doerr, A. Ivanova: Already moderate population sizes provably yield strong robustness to noise.
  • K. Perera, A. Neumann: Multi-objective evolutionary algorithms with sliding window selection for the dynamic chance-donstrained knapsack problem.
  • H. Q. Ngo, M. Guo, H. Nguyen: Optimizing cyber response time on temporal active directory networks using decoys.

GECCO 2023

The 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 collaborators have 9 accepted full papers for GECCO 2023:
  • A. Ivanova, D. Antipov, B. Doerr: Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise
  • A. Marrero, E. Segredo, E. Hart, J. Bossek, A. Neumann: Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space
  • J. Bossek, A. Neumann, F. Neumann: On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
  • F. Neumann, C. Witt: 3-Objective Pareto Optimization for Problems with Chance Constraints
  • A. Neumann, S. Goulder, X. Yan, G. Sherman, B. Campbell, M. Guo, F. Neumann: Evolutionary Diversity Optimization for the Detection and Concealment of Spatially Defined Communication Networks
  • D. Goel, A. Neumann, F. Neumann, H. Nguyen, M. Guo: Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems
  • T. Friedrich, T. Kötzing, A. Neumann, F. Neumann, A. Radhakrishnan: Analysis of (1+1) EA on LeadingOnes with Constraints
  • S. Baguley, T.Friedrich, A. Neumann, F. Neumann, M. Pappik, Z. Zeif: Fixed Parameter Multi-Objective Evolutionary Algorithms for the W-Separator Problem
  • A. Nikfarjam, R. Rothenberger, F. Neumann, T. Friedrich: Evolutionary Diversity Optimisation in Constructing Satisfying Assignments

AAAI, ECAI, IJCAI and NeurIPS 2023

The AAAI Conference on Artificial Intelligence (AAAI), the European Conference on Artificial Intelligence (ECAI), the International Joint Conference on Artificial Intelligence (IJCAI) and the Conference on Neural Information Processing Systems (NeurIPS) are leading international conferences in the area of artificial intelligence. We and our international collaborators have the following 5 full papers accepted at AAAI 2023, ECAI 2023, IJCAI 2023 and NeurIPS 2023:
  • Mingyu Guo, Max Ward, Aneta Neumann, Frank Neumann, Hung Nguyen: Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs. AAAI 2023.
  • Frank Neumann, Carsten Witt: Fast Pareto Optimization Using Sliding Window Selection. ECAI 2023.
  • Xiankun Yan, Anh Viet Do, Feng Shi, Xiaoyu Qin, Frank Neumann: Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties. ECAI 2023.
  • Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann: Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint. IJCAI 2023.
  • Anh Viet Do, Aneta Neumann, Frank Neumann, Andrew M. Sutton: Rigorous runtime analysis of MOEA/D for solving multi-objective minimum weight base problems. NeurIPS 2023.

CEC 2023

IEEE Congress on Evolutionary Computation (CEC) is one of the largest conferences in the field of evolutionary computation. We and our international collaborators have the following 2 full papers accepted at CEC 2023:
  • Frank Neumann, Aneta Neumann, Chao Qian, Anh Viet Do, Jacob de Nobel, Diederick Vermetten, Saba Sadeghi Ahouei, Furong Ye, Hao Wang, Thomas Bäck: Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler.
  • Michael Stimson, William Reid, Aneta Neumann, Simon Ratcliffe, Frank Neumann: Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting.

FOGA 2023

The ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA) is a premier event for research on the theory of evolutionary computation. We and our international collaborators have the following 3 full papers accepted at FOGA 2023:
  • Denis Antipov, Aneta Neumann, Frank Neumann: Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax.
  • Luke Branson, Andrew M. Sutton, Xiankun Yan: Finding Antimagic Labelings of Trees by Evolutionary Search.
  • Jack Kearney, Frank Neumann, Andrew M. Sutton: Fixed-Parameter Tractability of the (1 + 1) Evolutionary Algorithm on Random Planted Vertex Covers.

PPSN 2022

The 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 8 full papers accepted at PPSN 2022:
  • Adel Nikfarjam, Aneta Neumann, Jakob Bossek, Frank Neumann: Co-evolutionary Diversity Optimisation for the Traveling Thief Problem.
  • Adel Nikfarjam, Amirhossein Moosavi, Aneta Neumann, Frank Neumann: Computing High-Quality Solutions for the Patient Admission Scheduling Problem Using Evolutionary Diversity Optimisation.
  • Aneta Neumann, Yue Xie, Frank Neumann: Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits.
  • Yue Xie, Aneta Neumann, Ty Stanford, Charlotte Lund Rasmussen, Dorothea Dumuid, Frank Neumann: Evolutionary Time-Use Optimization for Improving Children's Health Outcomes.
  • Adel Nikfarjam, Anh Viet Do, Frank Neumann: Analysis of Quality Diversity Algorithms for the Knapsack Problem.
  • Feng Shi, Xiankun Yan, Frank Neumann: Runtime Analysis of Simple Evolutionary Algorithms for the Chance-Constrained Makespan Scheduling Problem.
  • Frank Neumann, Carsten Witt: Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions.
  • Tobias Friedrich, Timo Kötzing, Frank Neumann, Aishwarya Radhakrishnan: Theoretical Study of Optimizing Rugged Landscapes with the cGA.

GECCO 2022

Accepted full papers for GECCO 2022:

  • Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann: Niching-based Evolutionary Diversity Optimization for the Traveling Salesperson Problem.
  • Diksha Goel, Max Hector Ward-Graham, Aneta Neumann, Frank Neumann, Hung Nguyen: Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation.
  • Adel Nikfarjam, Aneta Neumann, Frank Neumann: On the Use of Quality Diversity Algorithms for The Traveling Thief Problem. (Nominated for Best Paper Award in the track "Evolutionary Combinatorial Optimization and Metaheuristics")
  • Aneta Neumann, Denis Antipov, Frank Neumann: Coevolutionary Pareto Diversity Optimization.
  • Frank Neumann, Dirk Sudholt, Carsten Witt: The Compact Genetic Algorithm Struggles on Cliff Functions.
  • Jakob Bossek, Frank Neumann: Exploring the Feature Space of TSP Instances Using Quality Diversity.
  • Adel Nikfarjam, Aneta Neumann, Frank Neumann: Evolutionary Diversity Optimisation for The Traveling Thief Problem.

GECCO 2020

Accepted full papers for GECCO 2020:

  • Jakob Bossek, Christian Grimme, Heike Trautmann: Dynamic Bi-Objective Routing of Multiple Vehicles.
  • Anh Viet Do, Jakob Bossek, Aneta Neumann, Frank Neumann: Evolving Diverse Sets of Tours for the Travelling Salesperson Problem.
  • Jakob Bossek, Carola Doerr, Pascal Kerschke: Initial Design Strategies and their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.
  • Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt: More Effective Evolutionary Algorithms for Graph Coloring Through Dynamic Optimisation.
  • Mehdi Neshat, Bradley Alexander, Nataliia Sergiienko, Markus Wagner: Optimisation of Large Wave Farms using Multi-strategy Evolutionary Frameworks.
  • Vahid Roostapour, Jakob Bossek, Frank Neumann: Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem.
  • Yue Xie, Aneta Neumann, Frank Neumann: Specific Single- and Multi-Objective Evolutionary Algorithms for the Chance-Constrained Knapsack Problem.
  • Jakob Bossek, Katrin Casel, Pascal Kerschke, Frank Neumann: The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. [CoRR abs/2002.01070]
  • Mahmoud Bokhari, Markus Wagner, Brad Alexander: Towards Rigorous Validation of Energy Optimisation Experiments.

AAAI 2020 and ECAI 2020

Accepted full papers at AAAI 2020 and ECAI 2020:

  • B. Doerr, C. Doerr, A. Neumann, F. Neumann, A. M. Sutton: Optimization of chance-constrained submodular functions. AAAI 2020.
  • H. Assimi, O. Harper, Y. Xie, A. Neumann, F. Neumann: Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives. ECAI 2020.
  • M. Hasani-Shoreh, R. Hermoza Aragonés, F. Neumann: Neural networks in evolutionary dynamic constrained optimization: computational cost and benefits. ECAI 2020.
  • V. Doskoc, T. Friedrich, A. Göbel, A. Neumann, F. Neumann, F. Quinzan: Non-Monotone submodular maximization with multiple knapsacks in static and dynamic settings. ECAI 2020.

FOGA 2019

Accepted full papers at FOGA 2019:

  • F. Neumann, A. Sutton: Runtime analysis of evolutionary algorithms for the chance-constrained knapsack problem.
  • F. Shi, F. Neumann, J. Wang: Runtime analysis of evolutionary algorithms for the depth restricted minimum spanning tree problem.
  • V. Roostapour, M. Pourhassan, F. Neumann: Analysis of baseline evolutionary algorithms for the Packing While Travelling problem.
  • J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann: Evolving diverse TSP instances by means of novel and creative mutation operators.

GECCO 2019

Accepted full papers at GECCO 2019:

  • F. Neumann, Pourhassan, Witt: Improved Runtime Results for Simple Randomised Search Heuristics on Linear Functions with a Uniform Constraint.
  • Doerr, Doerr, F. Neumann: Fast Re-Optimization via Structural Diversity. [CoRR abs/1902.00304]
  • Bossek, F. Neumann, Peng, Sudholt: Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.
  • A. Neumann, Gao, Wagner, F. Neumann: Evolutionary Diversity Optimization Using Multi-Objective Indicators. (Nominated for Best Paper Award in the track "Genetic Algorithms") [CoRR abs/1811.06804]
  • Bossek, Grimme, F. Neumann: On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.
  • Xie, Harper, Assimi, A. Neumann, F. Neumann: Evolutionary Algorithms for the Chance-Constrained Knapsack Problem. [CoRR abs/1902.04767]
  • Neshat, Alexander, Sergiienko, Wagner: A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters. (Best Paper Award in the track "Real World Applications")
  • Brownlee Petke, Alexander, Barr, Wagner, White: Gin: Genetic Improvement Research Made Easy.
  • Jakobovic, Picek, Ribeiro, Wagner: A characterisation of S-box fitness landscape in cryptography. [CoRR abs/1902.04724]

EU COST Action Short Term Scientific Missions

We are an international partner of the EU COST Action CA15140 - Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO). As part of this, we have the following researchers visiting our group to carry out a Short Term Scientific Mission (STSM):

New ARC Discovery Project and Humboldt Fellowship

AAAI 2019

Accepted full papers at AAAI 2019:

  • V. Roostapour, A. Neumann, F. Neumann, T. Friedrich: Pareto optimization for subset selection with dynamic cost constraints. Paper
  • T. Friedrich, A. Göbel, F. Neumann, F. Quinzan, R. Rothenberger: Greedy maximization of functions with bounded curvature under partition matroid constraints. [CoRR abs/1811.05351]
  • F. Neumann, A. M. Sutton: Evolving solutions to community-structured satisfiability formulas. Paper
  • T. Weise, Z. Wu, M. Wagner: An improved generic bet-and-run strategy with performance prediction for stochastic local search.

PPSN 2018

Accepted full papers 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

Research Consortium

Our 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.

Research Areas

The Optimisation and Logistics Group has close connections to the company Complexica based in Adelaide. Complexica is a leading provider of Artificial Intelligence software for optimising sales & marketing activities, ranging from promotional planning, pricing, and product mix optimisation, through to sales force automation, CRM, and multi-channel quoting and order processing.