- author = "Nadarajen Veerapen and Gabriela Ochoa",
- title = "Visualising the global structure of search landscapes: genetic improvement as a case study",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2018",
- volume = "19",
- number = "3",
- pages = "317--349",
- month = sep,
- note = "Special issue on genetic programming, evolutionary computation and visualization",
- keywords = "genetic algorithms, genetic programming, genetic improvement",
- ISSN = "1389-2576",
-
URL = "
http://hdl.handle.net/1893/27485",
-
URL = "http://hdl.handle.net/11667/120",
-
URL = "https://doi.org/10.1007/s10710-018-9328-1",
-
DOI = "
doi:10.1007/s10710-018-9328-1",
- size = "33 pages",
- abstract = "The search landscape is a common metaphor to describe the structure of computational search spaces. Different landscape metrics can be computed and used to predict search difficulty. Yet, the metaphor falls short in visualisation terms because it is hard to represent complex landscapes, both in terms of size and dimensionality. This paper combines local optima networks, as a compact representation of the global structure of a search space, and dimensionality reduction, using the t-distributed stochastic neighbour embedding algorithm, in order to both bring the metaphor to life and convey new insight into the search process. As a case study, two benchmark programs, under a genetic improvement bug-fixing scenario, are analysed and visualised using the proposed method. Local optima networks for both iterated local search and a hybrid genetic algorithm, across different neighbourhoods, are compared, highlighting the differences in how the landscape is explored.",
-
notes = "Triangle Program, TCAS, LON, t-SNE Research Data
http://hdl.handle.net/11667/120
'LibTooling library of Clang-LLVM to parse the programs, build the abstract syntax trees, and rewrite'",
