Genetic Improvement of Software
and SBSE

Genetic Improvement of Software
and SBSE
Special Session CEC 25-29 July 2016 Vancouver

Chinese dragon Stanley park

Location Vancouver Conference Centre, 25-29 July 2016.
West Building Level 2 Room: 205
Vancouver Convention Centre West
1055 Canada Place
Vancouver, BC
V6C 0C3
Canada
Weather
Publication IEEE Press
Programme wcci16-pbk-c.pdf (25 June)
Publication IEEE Press
Registration info

Aim and Scope

In the past ten years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to software engineering in which search-based optimisation algorithms are used to address problems. Evolutionary Computation (genetic algorithms, GAs, genetic programming, GP, ES, DE, GE, etc.) and other stochastic techniques are often used (SA, tabu, MCTS). The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. SBSE has been applied to a number of software engineering activities, right across the life-cycle from requirements engineering, project planning and cost estimation through testing, to automated maintenance, bug repair, porting, automatic parallelisation, performance improvements, service-oriented software engineering, compiler optimisation and quality assessment.

The GI@CEC-2016 special session at the 2016 IEEE World Congress on Computational Intelligence provides an opportunity to showcase recent breakthroughs in automatic improvement of Software using optimisation (particularly genetic, stochastic or search based) techniques. Such as: software transplanting, growing and grafting new code, genetic improvement, loop perforation, constraint based program synthesis and multi-objective Pareto trade-offs between functional and non-functional properties, such as speed, accuracy, solution quality, memory and improved efficiency.

We invite submissions on any aspect of SBSE, including, but not limited to, genetic improvement theoretical results and interesting new applications. The suggested topics cover the entire range of functional and non-functional properties:

Timetable Thursday, 28 July 2016. (Room: 205)

5:50--6:10 #E-16364
Product Selection Based on Upper Confidence Bound MOEA/D-DRA for Testing Software Product Lines
Thiago do Nascimento Ferreira, Josiel Neumann Kuk, Aurora Pozo, Silvia Regina Vergilio.
slides

6:10--6:30 #E-16921
Automatic Lock-free Parallel Programming on Multi-core Processors
Gopinath Chennupati, R Muhammad Atif Azad, Conor Ryan
YouTube mp4 video

Special Session Organisers

Markus Wagner, markus.wagner@ School of Computer Science The University of Adelaide, Australia adelaide.edu.au
W. B. Langdon, w.langdon@ Department of Computer Science, University College, London cs.ucl.ac.uk
Brad Alexander brad@ School of Computer Science The University of Adelaide, Australia cs.adelaide.edu.au

Publication

As with other WCCI papers, papers accepted by GI@CEC-2016 will be disseminated by the IEEE and become part of its electronic archive.

The Team

Dr. Markus Wagner (University of Adelaide, Australia)

Markus Wagner
Markus is a Lecturer at the School of Computer Science, University of Adelaide, Australia. He obtained his PhD studies at the Max Planck Institute for Informatics in Saarbruecken, Germany and at the University of Adelaide, Australia. His research topics range from mathematical runtime analysis of heuristic optimisation algorithms and theory-guided algorithm design to applications of heuristic methods to renewable energy production, professional team cycling and software engineering. He has written more than 50 articles with over 50 different co-authors in total.

Dr. W. B. Langdon (University College London)

stop the war
William B. Langdon's research includes using genetic programming to genetically improve existing software, search based software engineering and Bioinformatics. Indeed GI has been used to significantly improve a widely used Bioinformatics tool, nVidia software running on graphics hardware and a GPU kernel for NMR medical imaging registration. He co-organised the computational intelligence on GPUs (CIGPU) series of workshops and special sessions at WCCI (2008, 2010 and 2012) started the EvoPAR track in the European conference on applications of evolutionary computation and several times he has co-chaired EuroGP and GECCO. His books include A Field Guide to Genetic Programming, Genetic Programming + Data Structures and Advances in Genetic Programming 3. He also maintains the genetic programming bibliography.

Dr. Brad Alexander (University of Adelaide, Australia)


Brad's research interests include program optimisation, rewriting, genetic-programming (GP) - especially the discovery of recurrences and search-based-software-engineering. He has also supervised successful projects in the evolution of control algorithms for robots, the evolution of three-dimensional geological models, and the synthesis of artificial water distribution networks, and using background optimisation to improve the performance of instruction set simulators (ISS)'s. He has also worked on improving algorithms for the analysis of water distribution networks.

Programme Commitee

CEC 2016 SBSE Tutorial

Search Based Software Engineering: Foundations, Recent Advances, Challenges and Future Research Directions Marouane Kessentini

Other GI and SBSE sites

A few links to other pages interested in genetic improvement of software (GI)

Photographs of WCCI 2016

WCCI 2016

More Photographs of Vancouver

Pictures of Vancouver from:
WCCI 2006
Nic McPhee
CIRG.
HeuristicLab.
EvoCinv


W. B. Langdon 18 Nov 2015 (last update 8 August 2016)