Cooperative micro-particle swarm optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Cooperative micro-differential evolution for high-dimensional problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Natural and remote sensing image segmentation using memetic computing
IEEE Computational Intelligence Magazine
A digital watermarking scheme based on singular value decomposition and tiny genetic algorithm
Digital Signal Processing
Paired-bacteria optimiser - A simple and fast algorithm
Information Processing Letters
Self-tuned Evolution-COnstructed features for general object recognition
Pattern Recognition
Parallel cooperative micro-particle swarm optimization: A master-slave model
Applied Soft Computing
Image Segmentation Based on Bacterial Foraging and FCM Algorithm
International Journal of Swarm Intelligence Research
| Hi-index | 0.00 |
The expedience of today's image-processing applications is no longer based on the performance of a single algorithm alone. These systems appear to be complex frameworks with a lot of sub-tasks that are solved by specific algorithms, adaptation procedures, data handling, scheduling, and parameter choices. The venture of using computational intelligence (CI) in such a context, thus, is not a matter of a single approach. Among the great choice of techniques to inject CI in an image-processing framework, the primary focus of this presentation will be on the usage of so-called tiny-GAs. This stands for an evolutionary procedure with low efforts, i.e. small population size (like 10 individuals), little number of generations, and a simple fitness. Obviously, this is not suitable for solving highly complex optimization tasks, but the primary interest here is not the best individual's fitness, but the fortune of the algorithm and its population, which has just escaped the Monte-Carlo domain after random initialization. That this approach can work in practice will be demonstrated by means of selected image-processing applications, especially in the context of linear regression and line fitting; evolutionary post processing of various clustering results, in order to select a most suitable one by similarity; and classification by the fitness values obtained after a few generations