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GI@CEC 2016 : GI@CEC-2016 Genetic Improvement of Software | |||||||||||||||
Link: http://www0.cs.ucl.ac.uk/staff/w.langdon/cec2016/ | |||||||||||||||
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Call For Papers | |||||||||||||||
GI@CEC-2016 Genetic Improvement of Software submit by 15 January 2016.
We are very pleased to announce that there will be a special session at next year's CEC conference dedicated to Genetic Improvement of Software and Search Based Software Engineering (SBSE). CEC 2016 will be held in Vancouver (Canada) 25-29 July as part of the IEEE World Congress on Computational Intelligence, WCCI 2016. Although the Congress on Evolutionary Computation has been run every year since 1999, this will be the first session dedicated to genetic improvement (GI) of software. Many more details can be found on the GI@CEC-2016 web page http://www.cs.ucl.ac.uk/staff/W.Langdon/cec2016/ and the main http://www.wcci2016.org/ pages. The submission deadline (8 pages double column) is 15 January 2016. Submissions must in PDF and be made electronically via http://ieee-cis.org/conferences/cec2016/upload.php === Aim and Scope === There has been a dramatic increase in work on Search-Based Software Engineering (SBSE), the approach to software engineering in which search-based optimisation algorithms are used to solve software engineering problems. Evolutionary Computation (genetic algorithms, GAs, genetic programming, GP, ES, DE, GE, etc.) and other stochastic techniques are often used (SA, tabu, MCTS). One of the brightest areas is the use of stochastic search to automatically improve existing human written code. Recent successes have included automatic bug repair, porting, automatic parallelisation, and performance improvements. With increasing interest in software transplanting, growing and grafting new code, genetic improvement, loop perforation, and constraint based program synthesis And also multi-objective Pareto trade-offs between functional and non-functional properties, such as speed, accuracy, solution quality, memory and improved efficiency. SBSE 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, service-oriented software engineering, compiler optimisation and quality assessment. With this special session, we are providing an opportunity to showcase recent breakthroughs in this field. 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: * bandwidth minimisation * latency minimisation * fitness optimisation * energy optimisation * software specialisation * memory optimisation * software transplantation * bug fixing * multi-objective SE optimisation Markus Wagner markus.wagner@adelaide.edu.au Bill Langdon w.langdon@cs.ucl.ac.uk Brad Alexander brad@cs.adelaide.edu.au |
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