Modeling Hierarchical Architectures with Genetic Programming and Neuroscience Knowledge for Image Classification Through Inferential Knowledge

Created by W.Langdon from gp-bibliography.bib Revision:1.7325

@InProceedings{Olague:2022:GPTP,

• author = "Gustavo Olague and Matthieu Olague and Gerardo Ibarra-Vazquez and Isnardo Reducindo and Aaron Barrera and Axel Martinez and Jose Luis Briseno",
• title = "Modeling Hierarchical Architectures with Genetic Programming and Neuroscience Knowledge for Image Classification Through Inferential Knowledge",
• booktitle = "Genetic Programming Theory and Practice XIX",
• year = "2022",
• editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf",
• series = "Genetic and Evolutionary Computation",
• pages = "141--166",
• address = "Ann Arbor, USA",
• month = jun # " 2-4",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-981-19-8459-4",
• URL = "http://link.springer.com/chapter/10.1007/978-981-19-8460-0_7",
• DOI = "doi:10.1007/978-981-19-8460-0_7",
• abstract = "Brain programming is a methodology based on the idea that templates are necessary to describe artificial dorsal and ventral streams and their combination into an artificial visual cortex. We review the main concerns by introducing some initial thoughts about the status of genetic programming and other methodologies related to our research work. This chapter proposes the hierarchical integration of two architectures (templates) to enhance the quality of acquiring artificial visual percepts. We theoretically justified the necessity for designing manual hierarchical architectures. Planning complex structures through inferential knowledge simplify the design while adopting current technology. The methodology base its analysis on providing domain knowledge (neuroscience) at a higher level while looking for better computational structures within a local (lower) level. The efficiency of searching for optimal architectural configurations proceeds from deductive and inductive reasoning. This chapter brings a proposal of abductive reasoning to enrich the brain programming paradigm by taking advantage of computational re-use of dorsal stream discoveries while enhancing the overall complexity of the final proposal. We propose a Visual Turing test to establish the quality of the proposal in comparison with the state of the art. The results show that our methodology can produce consistent outcomes during training and testing, representing significant progress toward thought representation.",
• notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023",

}

Genetic Programming entries for Gustavo Olague Matthieu Olague Gerardo Ibarra-Vazquez Isnardo Reducindo Aaron Barrera Axel Martinez Jose Luis Briseno

Citations