- author = "Colin G. Johnson and John R. Woodward",
- title = "Fitness as Task-relevant Information Accumulation",
- booktitle = "Genetic Improvement 2015 Workshop",
- year = "2015",
- editor = "William B. Langdon and Justyna Petke and David R. White",
- pages = "855--856",
- address = "Madrid",
- publisher_address = "New York, NY, USA",
- month = "11-15 " # jul,
- organisation = "SIGEvo",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, information gain, model complexity",
- isbn13 = "978-1-4503-3488-4",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gi2015/fitness_as_task-relevant_information_accumulation.pdf",
-
DOI = "
doi:10.1145/2739482.2768428",
- size = "2 pages",
-
abstract = "If you cannot measure it, you cannot improve it. Lord
Kelvin
Fitness in GP/GI is usually a short-sighted greedy fitness function counting the number of satisfied test cases (or some other score based on error). If GP/GI is to be extended to successfully tackle full software systems, which is the stated domain of Genetic Improvement, with loops, conditional statements and function calls, then this kind of fitness will fail to scale. One alternative approach is to measure the fitness gain in terms of the accumulated information at each executed step of the program. This paper discusses methods for measuring the way in which programs accumulate information relevant to their task as they run, by building measures of this information gain based on information theory and model complexity.",
-
notes = "position paper, slides
http://gpbib.cs.ucl.ac.uk/gi2015/gi_2015_slides/johnson
",
