1–2 October 2019

The 2019 Workshop on AI-based Optimisation (AI-OPT 2019) will take place on 1–2 October 2019 at the University of Melbourne, Australia.

Artificial Intelligence based optimisation techniques such as constraint programming, evolutionary computation, heuristic search, mixed integer programming, and swarm intelligence have found many applications in solving highly complex and challenging optimisation problems. Application domains include important areas such as cybersecurity, economics, engineering, renewable energy, health and supply chain management.

The goal of this informal workshop is to bring together researchers from a wide range of AI-based optimisation areas, discuss current challenges, and foster collaborations. The workshop will consists of technical presentations and allow enough time for discussions and collaborations. PhD students and early career researchers are especially encouraged to participate.

Algorithmic approaches and topics include (but are not limited to):

• Algorithmic game theory
• Approximation algorithms
• Constraint programming
• Evolutionary computation
• Heuristics search
• Machine learning for optimisation
• Mechanism design
• Metaheuristics
• Mixed integer programming
• Parameterised algorithms
• Swarm intelligence

Important dates

Monday 15 July 2019

Abstract submission deadline

Tuesday 30 July 2019

Notification

1–2 October 2019

Workshop

Venue

Woodward Conference Centre
Level 10
185 Pelham Street
Carlton, Victoria

[View map]

Contact and organisers

Uwe Aickelin
The University of Melbourne, Australia
Email:

Frank Neumann
The University of Adelaide, Australia
Email: frank.neumann@adelaide.edu.au

Accepted presentations

Sergey Polyakovskiy, Rym M'Hallah: Just-in-time Batch Scheduling Problem Under Two-dimensional Bin Packing Constraints

Emir Demirović, Peter J. Stuckey, James Bailey, Jeffrey Chan, Tias Guns, Ramamohanarao Kotagiri and Christopher Leckie: Learning Input Parameters to Combinatorial Optimisation Problems Based on Historical Data

Nasrin Sultana, Dr Jeffrey Chan and Kai Qin: Solving Travelling Salesman Problems with Transfer Learning

Daniel Anderson, Gregor Hendel, Pierre Le Bodic and Merlin Viernickel: Clairvoyant Restarts in Branch-and-Bound Search Using Online Tree-Size Estimation

Yuan Sun, Xiaodong Li and Andreas Ernst: Using Statistical Measures and Machine Learning for Graph Reduction to Solve Maximum Weight Clique Problems

Yue Xie, Oscar Harper, Hirad Assimi, Aneta Neumann and Frank Neumann: Evolutionary Algorithms for the Chance-Constrained Knapsack Problem

Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann and Frank Neumann: Pareto Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives

Aneta Neumann, Wanru Gao, Markus Wagner and Frank Neumann: Evolutionary Diversity Optimization Using Multi-Objective Indicators

Hadi A. Khorshidi and Uwe Aickelin: Semi-supervised clustering via multi-objective optimization with application in medical informatics

Kate Smith-Miles, Mario Andres Munoz and Neelofar Neelofar: Instance Space Analysis for insightful analysis of algorithm strengths and weaknesses

Mahfouth Alghamdi, Christoph Treude and Markus Wagner: Summarising Heterogeneous Artefacts: A Subset Selection Problem

Andreas Ernst and Dhananjay Thiruvady: Using Solution Merging for Scheduling

Konstantin Shestak: Local Search Algorithm with Penalties for a Consistent Vehicle Routing Problem under the Shift Length Constraints

Markus Wagner: Simple On-the-Fly Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems

Marcus Gallagher: Towards Better Benchmarking of Metaheuristics With Machine Learning Problems

Ahmad Kazemi, Andreas Ernst, Mohan Krishnamoorthy and Pierre Le Bodic: The inline fuel delivery problem

Ali Babalhavaeji, Uwe Aickelin, Hadi Akbarzadeh Khorshidi and Karin Verspoor: Robust Classification for heterogeneous features

Michael Kirley, Dan Herring, Dean Pakravan: Exploring multi-objective fitness landscapes

Day 1 Program: 1 October

9:00–9:10: Welcome

9:10–10:40: Presentations

Just-in-time Batch Scheduling Problem Under Two-dimensional Bin Packing Constraints

Sergey Polyakovskiy, Rym M’Hallah

Using Solution Merging for Scheduling

Andreas Ernst and Dhananjay Thiruvady

Learning Input Parameters to Combinatorial Optimisation Problems Based on Historical Data

Emir Demirović, Peter J. Stuckey, James Bailey, Jeffrey Chan, Tias Guns, Ramamohanarao Kotagiri and Christopher Leckie

10:40–11:10: Break

11:10–12:40: Presentations

Introduction workshop participants

(30 minutes)

Evolutionary Diversity Optimization Using Multi-Objective Indicators

Aneta Neumann, Wanru Gao, Markus Wagner and Frank Neumann

Towards Better Benchmarking of Metaheuristics With Machine Learning Problems

Marcus Gallagher

12:40–14:00: Lunch

14:00–15:30: Presentations

Evolutionary Algorithms for the Chance-Constrained Knapsack Problem

Yue Xie, Oscar Harper, Hirad Assimi, Aneta Neumann and Frank Neumann

Semi-supervised clustering via multi-objective optimization with application in medical informatics

Hadi A. Khorshidi and Uwe Aickelin

Summarising Heterogeneous Artefacts: A Subset Selection Problem

Mahfouth Alghamdi, Christoph Treude and Markus Wagner

15:30–16:00: Break

16:00–17:00: Presentation

Instance Space Analysis for insightful analysis of algorithm strengths and weaknesses

Kate Smith-Miles, Mario Andres Munoz and Neelofar Neelofar

17:00: Drinks

Day 2 Program: 2 October

9:00–10:30: Presentations

Solving Travelling Salesman Problems with Transfer Learning

Nasrin Sultana, Dr Jeffrey Chan and Kai Qin

Using Statistical Measures and Machine Learning for Graph Reduction to Solve Maximum Weight Clique Problems

Yuan Sun, Xiaodong Li and Andreas Ernst

Simple On-the-Fly Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems

Markus Wagner

10:30–11:00: Break

11:00–12:30: Presentations

Robust Classification for heterogeneous features

Ali Babalhavaeji, Uwe Aickelin, Hadi Akbarzadeh Khorshidi and Karin Verspoor

Exploring multi-objective fitness landscapes

Michael Kirley, Dan Herring, Dean Pakravan

Pareto Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives

Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann and Frank Neumann

12:30–14:00: Lunch

14:00–15:00: Early career researcher discussion / grant opportunities

15:00–15:30: Break

15:30–17:00: Presentations

Clairvoyant Restarts in Branch-and-Bound Search Using Online Tree-Size Estimation

Daniel Anderson, Gregor Hendel, Pierre Le Bodic and Merlin Viernickel

The inline fuel delivery problem

Ahmad Kazemi, Andreas Ernst, Mohan Krishnamoorthy and Pierre Le Bodic

Local Search Algorithm with Penalties for a Consistent Vehicle Routing Problem under the Shift Length Constraints

Konstantin Shestak

Registrations for the 2019 Workshop on AI-Based Optimisation are now open.

Registration to this event is for both days of the workshop (October 1 and 2). Single day registrations are not available.

Registrations close 24 September 2019.

Register

For any registrations after this date, please email the event organisers directly.