Measures of correspondence between binary patterns
Image and Vision Computing
Fractals everywhere
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Fractal image compression: theory and application
Fractal image compression: theory and application
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Network Training Using Genetic Algorithms
Neural Network Training Using Genetic Algorithms
Fractal Geometry in Digital Imaging
Fractal Geometry in Digital Imaging
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Iterated Function Systems are popular techniques for generating selfsimilar fractals. An important practical problem in this field is that of obtaining the IFS code which approximates a given image with a certain prescribed accuracy (inverse IFS problem). In this paper we present an hybrid evolutive-genetic algorithm to solve the inverse IFS problem in two steps: First, an Evolutive Strategy (ES) is applied to identify a set of a fine transformations associated with selfsimilar structures within the image. Then, the best adapted transformations are combined forming an initial population of IFS models and a Genetic Algorithm (GA) is used to find the optimal IFS model. We show that this hybrid algorithm performs significantly better than one-step global evolutive or genetic algorithms which have been recently reported in the literature.