SSBSE’2015 is pleased to present our keynote speakers.



Title: Co-evolutionary Algorithms: A Useful Computational Abstraction? (slides)

Interest in co‐evolutionary algorithms was triggered in part with Hillis’ 1990 paper describing his success in using one to evolve sorting networks. Since then there have been heightened expectations for using this nature‐inspired technique to improve on the range and power of evolutionary algorithms for solving difficult computation problems. However, after more than two decades of exploring this promise, the results have been somewhat mixed. In this talk I summarize the progress made and the lessons learned with a goal of understanding how they are best used and identify a variety of interesting open issues that need to be explored in order to make further progress in this area.

Short bio:
Prof. Kenneth A. De Jong received his Ph.D. in Computer Science from the University of Michigan in 1975. He joined George Mason University in 1984 and is currently a Professor of Computer Science, head of the Evolutionary Computation Laboratory, and associate director of the Krasnow Institute.
His research interests include genetic algorithms, evolutionary computation, machine learning, and adaptive systems. He is currently involved in research projects involving the development of new evolutionary algorithm (EA) theory, the use of EAs as heuristics for NP-hard problems, and the application of EAs to the problem of learning task programs in domains such as robotics, diagnostics, navigation and game playing. He is also interested in experience-based learning in which systems must improve their performance while actually performing the desired tasks in environments not directly in their control or the control of a benevolent teacher. Support for these projects is provided by DARPA, ONR, and NRL.
He is an active member of the Evolutionary Computation research community and has been involved in organizing many of the workshops and conferences in this area. He is the founding editor-in-chief of the journal Evolutionary Computation (MIT Press), and a member of the board of ACM SIGEVO.



Title: Genetic Improvement of Software for Multiple Objectives (slides)

Genetic programming(GP) can increase computer program’s functional and non-functional performance. It can automatically port or refactor legacy code written by domain experts. Working with programmers it can grow and graft (GGGP) new functionality into legacy systems and parallel Bioinformatics GPGPU code. We review Genetic Improvement (GI) and SBSE research on evolving software.

Short bio:
Dr. William Langdon was research officer for the Central Electricity Research Laboratories and project manager and technical coordinator for Logica before becoming a prolific, internationally recognized researcher. He currently works at the Centre for Research on Evolution, Search and Testing, at University College London. He has written three books, including “A Field Guide to Genetic Programming”, edited six more, and published over a hundred papers in international conferences and journals.
He is the resource review editor for Genetic Programming and Evolvable Machines, and a member of the editorial board of Evolutionary Computation. He has been a co-organizer of many international conferences and workshops, including the first workshop on Genetic Improvement (GI-2015). He has given several tutorials at international conferences and was elected ISGEC Fellow for his contributions to the field of Evolutionary Computation. He also maintains the genetic programming bibliography website, a compendium of researchers and publications related to Genetic Programming.
Dr. Langdon has extensive experience designing and implementing GP systems, and is a leader in both the empirical and theoretical analysis of evolutionary systems. He also has broad experience both in industry and academic settings in biomedical engineering, drug design, and bioinformatics. His current research includes Genetic Improvement (GI), including Optimising Existing Software with Genetic Programming.