Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
Genetic Programming and Evolvable Machines
Fractal Image Compression and Recurrent Iterated Function Systems
IEEE Computer Graphics and Applications
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Grammatical Retina Description with Enhanced Methods
Proceedings of the European Conference on Genetic Programming
Genetic Programming for Feature Detection and Image Segmentation
Selected Papers from AISB Workshop on Evolutionary Computing
Evolving edge detectors with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Practical evaluation of efficient fitness functions for binary images
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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There are applications where a binary image is given and a shape is to be reconstructed from it with some kind of evolutionary algorithms. A solution for this problem usually highly depends on the fitness function. On the one hand fitness function influences the convergence speed of the EA. On the other hand, fitness computation is done many times, therefore the fitness computation itself has to be reasonably fast. This paper tries to define what "reasonably fast" means, by giving a definition for the efficiency. A definition alone is however not enough, therefore several fitness functions and function classes are defined, and their efficiencies are examined.