Genetic neural networks on MIMD computers
Genetic neural networks on MIMD computers
Morphological connected filters and intra-region smoothing for image segmentation
Morphological connected filters and intra-region smoothing for image segmentation
IEA/AIE '99 Proceedings of the 12th international conference on Industrial and engineering applications of artificial intelligence and expert systems: multiple approaches to intelligent systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Algorithms: Theory and Applications in Signal Processing
Learning Algorithms: Theory and Applications in Signal Processing
A Unified Signal Algebra Approach to Two-Dimensional Parallel Digital Signal Processing
A Unified Signal Algebra Approach to Two-Dimensional Parallel Digital Signal Processing
A genetic programming approach to reconfigure a morphological image processing architecture
International Journal of Reconfigurable Computing - Special issue on selected papers from the southern programmable logic conference (SPL2010)
A genetic programming based system for the automatic construction of image filters
Integrated Computer-Aided Engineering
| Hi-index | 0.00 |
The goal of this work is to propose a general-purpose crossover operator for real-coded genetic algorithms that is able to avoid the major problems found in this kind of approach such as the premature convergence to local optima, the weakness of genetic algorithms in local fine-tuning and the use of realcoded genetic algorithms instead of the traditional binary-coded problems. Mathematical morphology operations have been employed with this purpose adapting its meaning from other application fields to the generation of better individuals along the evolution in the convergence process. This new crossover technique has been called mathematical morphology crossover (MMX) and it is described along with the resolution of systematic experiments that allow to test its high speed of convergence to the optimal value in the search space.