Search Results for author: Emma Hart

Found 8 papers, 1 papers with code

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

no code implementations24 Mar 2022 Mohamad Alissa, Kevin Sim, Emma Hart

This contrasts to typical approaches to algorithm-selection which require a model to be trained using domain-specific instance features that need to be first derived from the input data.

Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers

no code implementations30 Jan 2022 Rui P. Cardoso, Emma Hart, David Burth Kurka, Jeremy V. Pitt

Using Neuroevolution combined with Novelty Search to promote behavioural diversity is capable of constructing high-performing ensembles for classification.

Comparison of atlas-based and neural-network-based semantic segmentation for DENSE MRI images

no code implementations29 Sep 2021 Elle Buser, Emma Hart, Ben Huenemann

Two segmentation methods, one atlas-based and one neural-network-based, were compared to see how well they can each automatically segment the brain stem and cerebellum in Displacement Encoding with Stimulated Echoes Magnetic Resonance Imaging (DENSE-MRI) data.

Semantic Segmentation

Optimisation and Illumination of a Real-world Workforce Scheduling and Routing Application via Map-Elites

no code implementations29 May 2018 Neil Urquhart, Emma Hart

The first is to evaluate whether ME can provide solutions of competitive quality to an Evolutionary Algorithm (EA) in terms of a single objective function, and the second to examine its ability to provide a repertoire of solutions that maximise user choice.

Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm

1 code implementation20 Apr 2018 Emma Hart, Andreas S. W. Steyven, Ben Paechter

The presence of functional diversity within a group has been demonstrated to lead to greater robustness, higher performance and increased problem-solving ability in a broad range of studies that includes insect groups, human groups and swarm robotics.

An Investigation of Environmental Influence on the Benefits of Adaptation Mechanisms in Evolutionary Swarm Robotics

no code implementations20 Apr 2018 Andreas Steyven, Emma Hart, Ben Paechter

In this paper, we address this question by analysing the performance of a swarm in a range of simulated, dynamic environments where a distributed evolutionary algorithm for evolving a controller is augmented with a number of different individual learning mechanisms.

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