Busca evolutiva por redes booleanas na tarefa de classificação de densidade

• Autor(a) principal: Mattos, Thiago de
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do Mackenzie
• Texto Completo: http://dspace.mackenzie.br/handle/10899/24478
• topic: redes booleanas; autômatos celulares; classificação de densidade; computação evolutiva; CNPQ::CIENCIAS EXATAS E DA TERRA

Boolean networks consist of nodes that represent binary variables, which are computed as a function of the values represented by their adjacent nodes. This local processing entails global behaviors, such as the convergence to _xed points, a behavior found in the context of the density classi_cation problem, where the aim is the network's convergence to a fixed point of the prevailing node value in the initial global configuration of the network; in other words, a global decision is targeted, but according to a constrained, non-global action. In this work, we rely on evolutionary searches in order to _nd rules and network topologies with good performance in the task. All nodes' neighborhoods are assumed to be de_ned by non-regular and bidirectional links, and the Boolean function of the network initialized by the local majority rule. Firstly, is carried out a search in the space of network topologies, guided by the ω metric, related to the "small-worldness" of the networks, and then, in the space of Boolean functions, but constraining the network topologies to the best family identified in the previous experiment..