Evolution of Artistic Image Variants Through Feature Based Diversity Optimisation

被引：31

作者：

Alexander, Brad ^{[1
]}

Kortman, James ^{[1
]}

Neumann, Aneta ^{[1
]}

机构：

[1] Univ Adelaide, Adelaide, SA 5005, Australia

来源：

PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17) | 2017年

关键词：

Evolutionary-art; features; diversity; constraints;

D O I：

10.1145/3071178.3071342

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：171 / 178

页数：8

共 17 条

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[2]

[Anonymous], 2016, P ADV NEURAL INFORM

[3]

Correia J, 2013, NEW URB ATL, P25

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