- author = "William B. Langdon and Gabriela Ochoa",
- title = "Genetic Improvement: A Key Challenge for Evolutionary Computation",
- booktitle = "Key Challenges and Future Directions of Evolutionary Computation",
- year = "2016",
- editor = "Yun Li",
- pages = "3068--3075",
- address = "Vancouver",
- month = "25-29 " # jul,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, genetic improvement",
- isbn13 = "978-1-5090-0623-6",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2016_cec.pdf",
-
DOI = "
doi:10.1109/CEC.2016.7744177",
- size = "8 pages",
- abstract = "Automatic Programming has long been a sub-goal of Artificial Intelligence (AI). It is feasible in limited domains. Genetic Improvement (GI) has expanded these dramatically to more than 100000 lines of code by building on human written applications. Further scaling may need key advances in both Search Based Software Engineering (SBSE) and Evolutionary Computation (EC) research, particularly on representations, genetic operations, fitness landscapes, fitness surrogates, multi objective search and co-evolution.",
-
notes = "Slides
http://www.cs.ucl.ac.uk/staff/W.Langdon/gggp/langdon_key_cec-2016_clean.pdf
Paper ID 17175. GISMO
WCCI2016",
