Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27485
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Visualising the Global Structure of Search Landscapes: Genetic Improvement as a Case Study
Author(s): Veerapen, Nadarajen
Ochoa, Gabriela
Keywords: fitness landscape
genetic improvement
local optima network
visualisation
Issue Date: 30-Sep-2018
Citation: Veerapen N & Ochoa G (2018) Visualising the Global Structure of Search Landscapes: Genetic Improvement as a Case Study. Genetic Programming and Evolvable Machines, 19 (3, Special Issue: SI), pp. 317-349. https://doi.org/10.1007/s10710-018-9328-1.
DAASE: Dynamic Adaptive Automated Software Engineering
EP/J017515/1
The Cartography of Computational Search Spaces
RPG-2015-395
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 (t-SNE) 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.
DOI Link: 10.1007/s10710-018-9328-1
Rights: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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