Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cully:2017:10.1109/TEVC.2017.2704781,
author = {Cully, A and Demiris, Y and Cully, AHR and Demiris, Y},
doi = {10.1109/TEVC.2017.2704781},
journal = {IEEE Transactions on Evolutionary Computation},
pages = {1--1},
title = {Quality and Diversity Optimization: A Unifying Modular Framework},
url = {http://dx.doi.org/10.1109/TEVC.2017.2704781},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - The optimization of functions to find the best solution according to one or several objectives has a central role in many engineering and research fields. Recently, a new family of optimization algorithms, named Quality-Diversity optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, Quality-Diversity algorithms are searching for a large collection of both diverse and high-performing solutions. The role of this collection is to cover the range of possible solution types as much as possible, and to contain the best solution for each type. The contribution of this paper is threefold. Firstly, we present a unifying framework of Quality-Diversity optimization algorithms that covers the two main algorithms of this family (Multi-dimensional Archive of Phenotypic Elites and the Novelty Search with Local Competition), and that highlights the large variety of variants that can be investigated within this family. Secondly, we propose algorithms with a new selection mechanism for Quality-Diversity algorithms that outperforms all the algorithms tested in this paper. Lastly, we present a new collection management that overcomes the erosion issues observed when using unstructured collections. These three contributions are supported by extensive experimental comparisons of Quality-Diversity algorithms on three different experimental scenarios.
AU - Cully,A
AU - Demiris,Y
AU - Cully,AHR
AU - Demiris,Y
DO - 10.1109/TEVC.2017.2704781
EP - 1
PY - 2017///
SN - 1089-778X
SP - 1
TI - Quality and Diversity Optimization: A Unifying Modular Framework
T2 - IEEE Transactions on Evolutionary Computation
UR - http://dx.doi.org/10.1109/TEVC.2017.2704781
UR - http://hdl.handle.net/10044/1/48539
ER -