Self-Regulated Artificial Ant Colonies on Digital Image Habitats Export

@article{citeulike:407712, abstract = {Artificial life models, swarm intelligent and evolutionary computation algorithms are usually built on fixed size populations. Some studies indicate however that varying the population size can increase the adaptability of these systems and their capability to react to changing environments. In this paper we present an extended model of an artificial ant colony system designed to evolve on digital image habitats. We will show that the present swarm can adapt the size of the population according to the type of image on which it is evolving and reacting faster to changing images, thus converging more rapidly to the new desired regions, regulating the number of his image foraging agents. Finally, we will show evidences that the model can be associated with the Mathematical Morphology Watershed algorithm to improve the segmentation of digital grey-scale images.}, author = {Fernandes, Carlos and Ramos, Vitorino and Rosa, Agostinho C.}, citeulike-article-id = {407712}, citeulike-linkout-0 = {http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_65.html}, keywords = {adaptive\_computation, ant\_colony\_systems, artificial\_life, classification, collective\_computing, collective\_intelligence, collective\_systems, complex\_systems, distributed\_computing, image\_classification, image\_processing, pattern\_recognition, recognition, segmentation, self-organization, stigmergy, swarm\_intelligence}, posted-at = {2005-11-24 18:57:31}, priority = {0}, title = {Self-Regulated Artificial Ant Colonies on Digital Image Habitats}, url = {http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_65.html} }

TY - JOUR ID - citeulike:407712 L3 - citeulike-article-id:407712 N2 - Artificial life models, swarm intelligent and evolutionary computation algorithms are usually built on fixed size populations. Some studies indicate however that varying the population size can increase the adaptability of these systems and their capability to react to changing environments. In this paper we present an extended model of an artificial ant colony system designed to evolve on digital image habitats. We will show that the present swarm can adapt the size of the population according to the type of image on which it is evolving and reacting faster to changing images, thus converging more rapidly to the new desired regions, regulating the number of his image foraging agents. Finally, we will show evidences that the model can be associated with the Mathematical Morphology Watershed algorithm to improve the segmentation of digital grey-scale images. TI - Self-Regulated Artificial Ant Colonies on Digital Image Habitats KW - adaptive_computation KW - ant_colony_systems KW - artificial_life KW - classification KW - collective_computing KW - collective_intelligence KW - collective_systems KW - complex_systems KW - distributed_computing KW - image_classification KW - image_processing KW - pattern_recognition KW - recognition KW - segmentation KW - self-organization KW - stigmergy KW - swarm_intelligence AU - Fernandes, Carlos AU - Ramos, Vitorino AU - Rosa, Agostinho UR - http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_65.html ER -