Written by: Thomas Weise

Created: 06 April 2023

• Benchmarking
• Traveling Salesman Problem
• Operations Research
• Evolutionary Computation
• Combinatorial Optimization
• Research Talk
• Genetic Algorithm
• Simulated Annealing
• Discrete Optimization
• Evolutionary Algorithm
• Maximum Satisfiability Problem
• Weise Thomas
• Theory
• Frequency Fitness Assignment
• Harbin Institute of Technology
• Shenzhen

Today, on 2023-04-06, I gave the invited talk Frequency Fitness Assignment (FFA) [频率适应度分配] at the group workshop of the Chair of AI and Aerodynamics led by Prof. Dr. Bernd R. NOACK at the Harbin Institute of Technology in Shenzhen, Guangdong, China [哈尔滨工业大学（深圳）]. The workshop had two sessions and featured eleven more highly interesting presentations, each outlining another exciting approach to aerodynamics and control. It was very cool to see the enormous progress that research has made in this field. Drones and air taxis will certainly have a huge and transformative impact on our society in the near future — and the talks clearly showed that optimization, machine learning, and AI will play an important role in both their design and control. It was a great honor to be invited by Prof. Noack to join this event and to have a chance to show our research there.

The talk on FFA is also available on our website, as slides pdf, as pre-packaged forty-minute video, and as a fifty-minute live recording from the workshop.

Read more: Prof. Weise gives Talk "Frequency Fitness Assignment" (频率适应度分配) on 2023-04-06 at the Chair of AI...

Written by: Thomas Weise

Created: 11 August 2021

• Benchmarking
• Optimization
• Education
• Statistics
• University of Adelaide
• Weise Thomas
• Wagner Markus

Today, on 2021-08-11, I had the great pleasure to give a guest lecture on "Comparing Optimization Algorithms" for the course "Evolutionary Computation" of Associate Professor Dr. Markus Wagner at the School of Computer Science of The University of Adelaide, Australia. The lesson was organized as a hybrid event, with both online attendants and students in the lecture room. We discussed several different topics, ranging from different views on performance (time-to-target vs. result-within-budget), different ways of measuring runtime (FEs, clock time), statistics (arithmetic mean vs. median vs. geometric mean, standard deviation vs. quantiles), statistical tests, and runtime behaviors. If you are interested in these topics, you can download my slides as well as a pre-recorded video that was intended as backup if the connection would fail. Fortunately, the connection did not fail and the lecture went smooth and nicely. I want to thank Dr. Wagner for this nice opportunity and also the audience for their patience and positive reception.

Read more: Guest Lesson "Comparing Optimization Algorithm" at the University of Adelaide

Written by: Thomas Weise

Created: 26 January 2021

• Benchmarking
• Operations Research
• Optimization
• Evolutionary Computation
• Machine Learning
• Statistics
• Computational Intelligence
• Weise Thomas
• Conference
• Workshop
• Yin Rongwang

This workshop continued our workshop series that we started in 2020 (BENCHMARK@GECCO with >75 participants and BENCHMARK@PPSN with >90 participants). The core theme is on benchmarking evolutionary computation methods and related sampling-based optimization heuristics, but each year, we will change the focus. For 2021, we aimed to have

• one session on general aspects of benchmarking (with 2-3 invited speakers and ample time for actual discussion)
• one session on reproducibility (see below for more details)

Read more: Good Benchmarking Practices for Evolutionary Computation (BENCHMARKING'21)

Written by: Thomas Weise

Created: 14 December 2020

• Benchmarking
• Operations Research
• Optimization
• Evolutionary Computation
• Machine Learning
• Statistics
• Computational Intelligence
• Li Xinlu
• Weise Thomas
• Conference
• Workshop
• Yin Rongwang

2021 Genetic and Evolutionary Computation Conference (GECCO'21)

Lille, France, July 10-14, 2021
http://iao.hfuu.edu.cn/aaboh21

 

The Analysing Algorithmic Behaviour of Optimisation Heuristics Workshop (AABOH), as part of the 2021 Genetic and Evolutionary Computation Conference (GECCO'21), invited the submission of original and unpublished research papers. Here you can download the AABOH Special Session Call for Papers (CfP) in PDF format and here as plain text file.

Optimisation and Machine Learning tools are among the most used tools in the modern world with its omnipresent computing devices. Yet, the dynamics of these tools have not been analysed in detail. Such scarcity of knowledge on the inner workings of heuristic methods is largely attributed to the complexity of the underlying processes that cannot be subjected to a complete theoretical analysis. However, this is also partially due to a superficial experimental set-up and, therefore, a superficial interpretation of numerical results. Indeed, researchers and practitioners typically only look at the final result produced by these methods. Meanwhile, the vast amount of information collected over the run(s) is wasted. In the light of such considerations, it is now becoming more evident that such information can be useful and that some design principles should be defined that allow for online or offline analysis of the processes taking place in the population and their dynamics.

Hence, with this workshop, we call for both theoretical and empirical achievements identifying the desired features of optimisation and machine learning algorithms, quantifying the importance of such features, spotting the presence of intrinsic structural biases and other undesired algorithmic flaws, studying the transitions in algorithmic behaviour in terms of convergence, any-time behaviour, performances, robustness, etc., with the goal of gathering the most recent advances to fill the aforementioned knowledge gap and disseminate the current state-of-the-art within the research community.

Read more: Analysing Algorithmic Behaviour of Optimisation Heuristics Workshop (AABOH'21)