Can’t See the Forest: Using an Evolutionary Algorithm to Produce an Animated Artwork

Trist, Karen; Ciesielski, Victor; Barile, Perry

doi:10.1007/978-3-642-11577-6_32

Cited by 5 publications

(1 citation statement)

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“…Experiment Two. Some literatures claimed that using the GA can generate art images [21,22]; in view of this, an interesting experiment using GA to generate the Mona Lisa's image is designed in this research. Taking 60 * 60 standard image of Mona Lisa as the comparison standard and the values of 60 * 60 pixels as design variables, a chromosome is made up of 360 genes, each of which has state of either 0 or 1; while 0 indicates that the pixel is white, 1 indicates that the pixel is black.…”

Section: Two Simulation Experiments Of Value Advantagementioning

confidence: 99%

Simulation Experiment Exploration of Genetic Algorithm’s Convergence over the Relationship Advantage Problem

Luo

2016

Mathematical Problems in Engineering

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Concentrating on the convergence analysis of Genetic Algorithm (GA), this study originally distinguishes two types of advantage sources: value advantage and relationship advantage. Accordingly, the quantitative feature, complete quantization feature, and the partial quantization feature in the fitness evaluation are proposed. Seven simulation experiments show that these two types of advantages have different convergence properties. For value advantage problems, GA has a good convergence. However, for a relationship advantage problem, only from the practical point of view, it is possible to get a feasible and even satisfactory solution through large-scale searching, but, in theory, however, the searching process is not convergent. Therefore, GA is not reliable to solve relationship advantage problems, to which most engineering problems involving combinatorial optimization belong. This study systematically shows convergence properties of “relationship advantage” through simulation experiments, which will be a new area for the further study on GA.

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Section: Two Simulation Experiments Of Value Advantagementioning

confidence: 99%

Simulation Experiment Exploration of Genetic Algorithm’s Convergence over the Relationship Advantage Problem

Luo

2016

Mathematical Problems in Engineering

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Evolving Mondrian-Style Artworks

Cohen

Cherchiglia

Costa

2017

Computational Intelligence in Music, Sound, Art and Design

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Evolutionary Associative Memories through Genetic Programming

Villegas-Cortéz

Sossa

Avilés‐Cruz

et al. 2012

Parallel Architectures and Bioinspired Algorithms

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Recibido el 23 de octubre de 2009; aceptado el 10 de enero de 2010Associative Memories (AMs) are useful devices designed to recall output patterns from input patterns. Each input-output pair forms an association. Thus, AMs store associations among pairs of patterns. An important feature is that since its origins AMs have been manually designed. This way, during the last 50 years about 26 different models and variations have been reported. In this paper, we illustrate how new models of AMs can be automatically generated through Genetic Programming (GP) based methodology. In particular, GP provides a way to successfully facilitate the search for an AM in the form of a computer program. The efficiency of the proposal was conducted by means of two tests based on binary and real-valued patterns. The experimental results show that it is possible to automatically generate AMs that achieve good results for the selected pattern recognition problems. This opens a new research area that allows, for the first time, synthesizing new AMs to solve specific problems.

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Can’t See the Forest: Using an Evolutionary Algorithm to Produce an Animated Artwork

Cited by 5 publications

References 1 publication

Simulation Experiment Exploration of Genetic Algorithm’s Convergence over the Relationship Advantage Problem

Simulation Experiment Exploration of Genetic Algorithm’s Convergence over the Relationship Advantage Problem

Evolving Mondrian-Style Artworks

Evolutionary Associative Memories through Genetic Programming

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