Minimising line segments in linear diagrams is NP-hard

Autor: Peter Chapman, Kevin Sim, Huang Hao Chen

Linear diagrams have been shown to be an effective method of representing set-based data. Moreover, a number of guidelines have been proven to improve the efficacy of linear diagrams. One of these guidelines is to minimise the number of line segments

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04d9fc55438e25de936cd9df960db926

Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches

Autor: Mohamad Alissa, Kevin Sim, Emma Hart

We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. Specifically we train two types of recurrent neural netw

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3970eb6c91ff736862c8258c2595aee8
http://arxiv.org/abs/2203.13392

A Neural Approach to Generation of Constructive Heuristics

Autor: Mohamad Alissa, Emma Hart, Kevin Sim

Publikováno v: CEC

Both algorithm-selection methods and hyper-heuristic methods rely on a pool of complementary heuristics. Improving the pool with new heuristics can improve performance, however, designing new heuristics can be challenging. Methods such as genetic pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ebeda91020e0ac0ab8765e0487b3c63f
https://doi.org/10.1109/cec45853.2021.9504989

Spatial biology of Ising-like synthetic genetic networks

Autor: Kevin Simpson, Alfredo L’Homme, Juan Keymer, Fernán Federici

Publikováno v: BMC Biology, Vol 21, Iss 1, Pp 1-16 (2023)

Abstract Background Understanding how spatial patterns of gene expression emerge from the interaction of individual gene networks is a fundamental challenge in biology. Developing a synthetic experimental system with a common theoretical framework th

Externí odkaz: https://doaj.org/article/2ad405c013344d48b6725d46beb6b3bd

Use of machine learning techniques to model wind damage to forests

Autor: Emma Hart, Dominique Guyon, Kana Kamimura, Barry Gardiner, Kevin Sim, Céline Meredieu

Publikováno v: Agricultural and Forest Meteorology
Agricultural and Forest Meteorology, Elsevier Masson, 2019, 265, pp.16-29. ⟨10.1016/j.agrformet.2018.10.022⟩

This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed indi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01ee72e202aee64c28134e07f8ba474c