Automatic Configuration and Learning for Evolutionary Computation

Much research in evolutionary computation focuses on the design and application of new algorithm variants for obtaining better results for hard application problems. For evolution strategies, a class of evolutionary algorithms, I will briefly discuss a few variants and their performance, and then illustrate a typical application example from the domain of industrial engineering design (such as ships, cars, etc.).

I will argue, however, that the traditional, expert-driven algorithm development approach has its limitations, and that there are suitable approaches towards an automatic configuration of evolutionary algorithms in general, and evolution strategies in particular. This is motivated by some early work on the (hyper-parameter ) optimization of genetic algorithms. Recent research in my group resulted in the configurable CMA-evolution strategy, which makes 4.308 variants of evolution strategies possible - most of which have never been tried before. This generalization can not only be used for searching through configuration space, but the optimization results can also be analyzed using data-driven approaches, resulting in a better understanding of the search mechanism.

In my perspective, this is only the beginning, since the results clearly indicate that the algorithm configuration should also be able to switch during the optimization run, and that the switching strategy could add an additional benefit for the algorithm’s convergence runtime. Ultimately, it would be great if the algorithm could continuously learn the changing requirements for module activations, which requires internal machine learning mechanisms such as multi-armed bandit or reinforcement learning approaches. For both topics, I will discuss our first results and outline the potential for further research. The talk concludes by providing a short overview of research topics and industrial projects in my research group.

Thomas Bäck

Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Netherlands

Thomas Bäck is professor of Computer Science at the Leiden Institute of Advanced Computer Science, Leiden University, Netherlands, since 2002. He received his PhD in Computer Science from Dortmund University, Germany, in 1994, and was leader of the Center for Applied Systems Analysis at the Informatik Centrum Dortmund until 2000. Until 2007, Thomas was also CTO of NuTech Solutions, Inc. (Charlotte, NC), where he gained ample experience in solving real-world problems in optimization and data analytics, by working with global enterprises in the automotive and other industry sectors.

Thomas received the IEEE Computational Intelligence Society (CIS) Evolutionary Computation Pioneer Award for his contributions in synthesizing evolutionary computation (2015), was elected as a fellow of the International Society of Genetic and Evolutionary Computation (ISGEC) for fundamental contributions to the field (2003), and received the best dissertation award from the "Gesellschaft für Informatik" in 1995.

Thomas has more than 300 publications, as well as two books on evolutionary algorithms: Evolutionary Algorithms in Theory and Practice (1996), Contemporary Evolution Strategies (2013). He is co-editor of the Handbook of Evolutionary Computation and the Handbook of Natural Computing. Thomas is also Co-Editor-in-Chief of the Natural Computing Series (Springer) and the journal Theoretical Computer Science C (Elsevier), and editorial board member of various journals.

Thomas’ research interests are in foundations and applications of evolutionary computation, efficient global optimization, and multiple objective optimization. His recent research also addresses optimization methods in machine learning, e.g., for hyperparameter optimization, as well as supervised and unsupervised machine learning for smart industry applications.

Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone?

In this keynote I introduce the use of Predictive Analytics for Software Engineering (SE) and then focus on the use of search-based heuristics to tackle long-standing SE prediction problems including (but not limited to) software development effort estimation and software de- fect prediction. I review recent research in Search-Based Predictive Mod- elling for SE in order to assess the maturity of the field and point out promising research directions. I conclude my keynote by discussing best practices for a rigorous and realistic empirical evaluation of search-based predictive models, a condicio sine qua non to facilitate the adoption of prediction models in software industry practices.

Federica Sarroa

Department of Computer Science, University College London

Federica Sarro is an Associate Professor at University College London in the Department of Computer Science. Her research covers Predictive Analytics for Software Engineering (SE), Empirical SE and Search-Based SE, with a focus on predictive modelling for software management and quality, software sizing, software testing and mobile app store analysis. Dr Sarro has published over 60 papers in prestigious software engineering conferences and journals and also received several international awards, including four best paper awards and the GECCO-HUMIES awarded for the human-competitive results achieved by her work on multi-objective effort estimation. She is an active member of the Software Engineering community: Over the last four years she has organised and chaired more than 15 international events and served on more than 50 program committees, receiving in 2018 the ACM distinguished reviewer award at ICSE'18. In 2018 she has been elected as Chair of the Steering Committee of the International Symposium on Search-Based Software Engineering (SSBSE), after having served it as a member for 3 years. Dr Sarro has also been Associate Editor of several SE journals, including the Empirical Software Engineering (EMSE) journal, and Guest Editor for the journals IEEE Transactions on Evolutionary Computation (TEVC) and Elsevier Information and Software Technology (IST).