Evolving Neural Networks for Multi-robot Odor Search

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Macedo:2016:ICARSC,

• author = "Joao Macedo and Lino Marques and Ernesto Costa",
• booktitle = "2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC)",
• title = "Evolving Neural Networks for Multi-robot Odor Search",
• year = "2016",
• pages = "288--293",
• abstract = "The tasks of odour detection, plume tracking and odour source localization constitute an important, yet complex, real world problem. One possible solution for them is based on the use of a group of mobile robots whose controllers have to be defined. Artificial Neural Networks (ANN) have already been used as controllers, but the task of hand defining their topology and parameters can be very challenging and time consuming. In this paper, we propose an approach to evolve, rather than design, ANN-based controllers. Our approach relies on Genetic Programming (GP), a family of stochastic search procedures loosely inspired by the biological principles of Natural Selection and Genetics. We compare our approach with a classic one, inspired by the chemotaxis behaviour of the E. coli bacteria. Our results show that this approach is able to outperform the chemotaxis in the experiments performed.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICARSC.2016.37",
• month = may,
• notes = "Also known as \cite{7781991}",

}

Genetic Programming entries for Joao Macedo Lino Marques Ernesto Costa

Citations