Fractals everywhere
The Science of Fractal Images
Genetic-algorithm-based learning
Machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
How to solve it: modern heuristics
How to solve it: modern heuristics
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using Multi-chromosomes to Solve a Simple Mixed Integer Problem
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
An extension of vose's markov chain model for genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Foundations of Global Genetic Optimization
Foundations of Global Genetic Optimization
Solving iterated functions using genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Evolutionary viral-type algorithm for the inverse problem for iterated function systems
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Grammar-based immune programming
Natural Computing: an international journal
An evolutionary-neural algorithm for solving inverse IFS problem for images in two-dimensional space
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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This paper presents an approach to the IFS inverse problem based on evolutionary computations. Having a bitmap image, we look for a set of functions that can reproduce a good approximation of a given image. A method using a variable number of mappings is proposed. A number of different crossover operators is described and tested. The possibility of enriching evolutionary algorithms by a specific type mechanism characteristic for replication of influenza viruses is discussed. The genetic material of the influenza type A virus consists of eight separate segments. In some types of tasks, such a structure of a genome can be more adequate than representation that consists of one sequence only. If influenza virus strains infect the same cell, then their RNA segments can mix freely, producing progeny viruses which represents the reasortment mechanism. Furthermore, mistakes leading to new mutations are common. The structure of problems for which such viral reproduction mechanisms can be effective are analyzed. The paper ends with some experimental results showing the images we were able to generate with the proposed method. The preliminary experimental results suggest that the introduction of the reasortment operator results in achieving satisfactory images in a smaller number of generations.