Advanced animation and rendering techniques
Advanced animation and rendering techniques
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Wavelets for computer graphics: theory and applications
Wavelets for computer graphics: theory and applications
Eons of genetically evolved algorithmic images
Creative evolutionary systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
RenderMan Companion: A Programmer's Guide to Realistic Computer Graphics
RenderMan Companion: A Programmer's Guide to Realistic Computer Graphics
Texturing and Modeling
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Genshade: an evolutionary approach to automatic and interactive procedural texture generation
Genshade: an evolutionary approach to automatic and interactive procedural texture generation
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows
Applied Intelligence
Evolving images for entertainment
IE '07 Proceedings of the 4th Australasian conference on Interactive entertainment
Evolving art using multiple aesthetic measures
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Learning aesthetic judgements in evolutionary art systems
Genetic Programming and Evolvable Machines
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This paper investigates the application of evolutionary multiobjective optimization to two-dimensional procedural texture synthesis. Genetic programming is used to evolve procedural texture formulae. Earlier work used multiple feature tests during fitness evaluation to rate how closely a candidate texture matches visual characteristics of a target texture image. These feature test scores were combined into all overall fitness score using a weighted sum. This paper improves this research by replacing the weighted sum with a Pareto ranking scheme, which preserves the independence of feature tests during fitness evaluation. Three experiments were performed: a pure Pareto ranking scheme, and two Pareto experiments enhanced with parameterless population divergence strategies. One divergence strategy is similar to that used by the NSGA-II system, and scores individuals using their nearest-neighbour distance in feature-space. The other strategy uses a normalized, ranked abstraction of nearest neighbour distance. A result of this work is that acceptable textures can be evolved much more efficiently and with less user intervention with MOP evolution than compared to the weighted sum approach. Although the final acceptability of a texture is ultimately a subjective decision of the user, the proposed use of multi-objective evolution is useful for generating for the user a diverse assortment of possibilities that reflect the important features of interest.