|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Author(s):||Langdon, William B|
|Title:||Visualising the Search Landscape of the Triangle Program|
|Citation:||Langdon WB, Veerapen N & Ochoa G (2017) Visualising the Search Landscape of the Triangle Program. In: McDermott J, Castelli M, Sekanina L, Haasdijk E & García-Sánchez P (eds.) EuroGP 2017: Genetic Programming. Lecture Notes in Computer Science, 10196. The 20th European Conference on Genetic Programming (EuroGP), Amsterdam, The Netherlands, 19.04.2017-21.04.2017. Cham, Switzerland: Springer, pp. 96-113. https://doi.org/10.1007/978-3-319-55696-3_7|
|Series/Report no.:||Lecture Notes in Computer Science, 10196|
|Conference Name:||The 20th European Conference on Genetic Programming (EuroGP)|
|Conference Dates:||2017-04-19 - 2017-04-21|
|Conference Location:||Amsterdam, The Netherlands|
|Abstract:||High order mutation analysis of a software engineering benchmark, including schema and local optima networks, suggests program improvements may not be as hard to find as is often assumed. 1) Bit-wise genetic building blocks are not deceptive and can lead to all global optima. 2) There are many neutral networks, plateaux and local optima, nevertheless in most cases near the human written C source code there are hill climbing routes including neutral moves to solutions.|
|Status:||AM - Accepted Manuscript|
|Rights:||Publisher policy allows this work to be made available in this repository; The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-55696-3_7|
|triangle.pdf||Fulltext - Accepted Version||947.04 kB||Adobe PDF||View/Open|
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