2021 Genetic and Evolutionary Computation Conference (GECCO'21)

Lille, France, July 10/11, 2021

A platform to come together and to discuss recent progress and challenges in the area of benchmarking optimization heuristics.

This workshop will continue 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 aim to have

Call for Papers Call for Papers

2021 Genetic and Evolutionary Computation Conference (GECCO'21)

Lille, France, July 10-14, 2021


The Analysing Algorithmic Behaviour of Optimisation Heuristics Workshop (AABOH), as part of the 2021 Genetic and Evolutionary Computation Conference (GECCO'21), is cordially inviting the submission of original and unpublished research papers.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.

Call for Papers Call for Papers

2021 IEEE Congress on Evolutionary Computation (CEC 2021)

June 28-July 1, 2021 in Kraków, Poland


The Special Session on Benchmarking of Computational Intelligence Algorithms (BOCIA), as part of the 2021 IEEE Congress on Evolutionary Computation (CEC 2021), is cordially inviting the submission of original and unpublished research papers. Here you can download the BOCIA Special Session Call for Papers (CfP) in PDF format and here as plain text file.

Computational Intelligence (CI), including Evolutionary Computation, Optimization, Machine Learning, and Artificial Intelligence, is a huge and expanding field which is rapidly gaining importance, attracting more and more interests from both academia and industry. It includes a wide and ever-growing variety of optimization and machine learning algorithms, which, in turn, are applied to an even wider and faster growing range of different problem domains. For all of these domains and application scenarios, we want to pick the best algorithms. Actually, we want to do more, we want to improve upon the best algorithm. This requires a deep understanding of the problem at hand, the performance of the algorithms we have for that problem, the features that make instances of the problem hard for these algorithms, and the parameter settings for which the algorithms perform the best. Such knowledge can only be obtained empirically, by collecting data from experiments, by analyzing this data statistically, and by mining new information from it. Benchmarking is the engine driving research in the fields of Computational Intelligence for decades, while its potential has not been fully explored.

The goal of this special session is to solicit original works on the research in benchmarking: Works which contribute to the domain of benchmarking of algorithms from all fields of Computational Intelligence, by adding new theoretical or practical knowledge. Papers which only apply benchmarking are not in the scope of the special session.

This special session is technically supported by the IEEE CIS Task Force on Benchmarking.

In the afternoon of September 22, 2020, the Mid-Autumn Festival and National Holiday Celebration for Foreign Experts [2020年度在皖高层次外国专家迎中秋庆国庆活动] of our province Anhui [安徽] took place at the Anhui Museum of Innovation [安徽创新馆]. The event was co-organized by the Department of Science and Technology of the Province Anhui [安徽省科学技术厅], the Hefei Municipal Bureau of Science and Technology [合肥市科学技术局], the Public Relations Department of the Provincial Party Committee [安徽省委宣传部], and the museum itself. More than 30 foreign experts from more than 10 different countries attended this event – a very high number given the current international COVID-19 pandemic. Together with Mr. Lei HONG [洪磊] of our International Office, I attended this event as representatives of our Hefei University [合肥学院].

On September 15, 2020, Prof. Weise introduced the team of our Institute of Applied Optimization (IAO) [应用优化研究所] to the fresh graduate students of our the School of Artificial Intelligence and Big Data [人工智能与大数据学院]. Each of our team members follows a different research direction within the overall field of Computational Intelligence, Optimization, Machine Learning, and Artificial Intelligence. Within the framework of our research, we can offer science-centric graduate student supervision. The presentation of our group's work has been received well, and several graduate students will join our team soon. We are looking forward to welcome them!

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