Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/128559
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Type: Conference paper
Title: Evolution of artistic image variants through feature based diversity optimisation
Author: Alexander, B.
Kortman, J.
Neumann, A.
Citation: Proceedings of the 2017 Genetic and Evolutionary Computation Conference (GECCO'17), 2017, pp.171-178
Publisher: Association for Computing Machinery (ACM)
Publisher Place: New York, NY
Issue Date: 2017
ISBN: 9781450349208
Conference Name: Genetic and Evolutionary Computation Conference (GECCO) (15 Jul 2017 - 19 Jul 2017 : Berlin, Germany)
Statement of
Responsibility: 
Brad Alexander, James Kortman, Aneta Neumann
Abstract: Measures aimed to improve the diversity of images and image features in evolutionary art help to direct search toward more novel and creative parts of the artistic search domain. To date such measures have not focused on selecting from all individuals based on their contribution to diversity of feature metrics. In recent work on TSP problem instance classification, selection based on a direct measure of each individual's contribution to diversity was successfully used to generate hard and easy TSP instances. In this work we use this search framework to evolve diverse variants of a source image in one and two feature dimensions. The resulting images show the spectrum of effects from transforming images to score across the range of each feature. The results also reveal interesting correlations between feature values in two dimensions.
Keywords: Evolutionary-art; features; diversity; constraints
Rights: © 2017 ACM.
DOI: 10.1145/3071178.3071342
Published version: https://dl.acm.org/doi/proceedings/10.1145/3071178
Appears in Collections:Aurora harvest 8
Computer Science publications

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