Landscape analysis under measurement error
Pages 1415 - 1418
Abstract
There are situations where the need for optimisation with a global precision tolerance arises - for example, due to measurement, numerical or evaluation errors in the objective function. In such situations, a global tolerance ϵ > 0 can be predefined such that two objective values are declared equal if the absolute difference between them is less than or equal to ϵ. This paper presents an overview of fitness landscape analysis under such conditions. We describe the formulation of common landscape categories in the presence of a global precision tolerance. We then proceed by discussing issues that can emerge as a result of using tolerance, such as the increase in the neutrality of the fitness landscape. To this end, we propose two methods to exhaustively explore plateaus in such application domains - one of which is point-based and the other of which is set-based.
References
[1]
Ozgur E. Akman, David A. Rand, Paul E. Brown, and Andrew J. Millar. 2010. Robustness from flexibility in the fungal circadian clock. BMC Systems Biology 4, 1 (24 Jun 2010), 88.
[2]
Ozgur E. Akman, Steven Watterson, Andrew Parton, Nigel Binns, Andrew J. Millar, and Peter Ghazal. 2012. Digital clocks: simple Boolean models can quantitatively describe circadian systems. Journal of The Royal Society Interface 9, 74 (2012), 2365--2382.
[3]
Khulood Alyahya and Jonathan E. Rowe. 2019. Landscape Analysis of a Class of NP-Hard Binary Packing Problems. Evolutionary Computation 27, 1 (2019), 47--73. 30365387.
[4]
Kevin Doherty, Khulood Alyahya, Ozgur E. Akman, and Jonathan E. Fieldsend. 2017. Optimisation and Landscape Analysis of Computational Biology Models: A Case Study. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '17). ACM, New York, NY, USA, 1644--1651.
[5]
Edgar Galván-López and Riccardo Poli. 2006. An Empirical Investigation of How and Why Neutrality Affects Evolutionary Search. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO '06). ACM, New York, NY, USA, 1149--1156.
[6]
Holger H Hoos and Thomas Stützle. 2004. Stochastic local search: Foundations and applications. Elsevier.
[7]
Yaochu Jin and Jürgen Branke. 2005. Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on evolutionary computation 9, 3 (2005), 303--317.
[8]
Marie-Eléonore Marmion, Clarisse Dhaenens, Laetitia Jourdan, Arnaud Liefooghe, and Sébastien Verel. 2011. NILS: A Neutrality-Based Iterated Local Search and Its Application to Flowshop Scheduling. In Evolutionary Computation in Combinatorial Optimization, Peter Merz and Jin-Kao Hao (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 191--202.
[9]
Gabriela Ochoa. 2006. Error Thresholds in Genetic Algorithms. Evolutionary Computation 14, 2 (2006), 157--182.
[10]
Christian M. Reidys and Peter F. Stadler. 2001. Neutrality in fitness landscapes. Appl. Math. Comput. 117, 2 (2001), 321 -- 350.
[11]
Sébastien Verel, Philippe Collard, Marco Tomassini, and Leonardo Vanneschi. 2007. Fitness landscape of the cellular automata majority problem: View from the "Olympus". Theoretical Computer Science 378, 1 (2007), 54 -- 77.
[12]
Sébastien Verel, Gabriela Ochoa, and Marco Tomassini. 2011. Local Optima Networks of NK Landscapes With Neutrality. IEEE Transactions on Evolutionary Computation 15, 6 (Dec 2011), 783--797.
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Published In
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
- Editor:
- Manuel López-Ibáñez,
- General Chairs:
- Anne Auger,
- Thomas Stützle
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Published: 13 July 2019
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