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Minimization of Systemic Risk for Directed Network Using Genetic Algorithm Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Brain Programming and the Random Search in Object Categorization

Authors : Gustavo Olague, Eddie Clemente, Daniel E. Hernández, Aaron Barrera

Published in: Applications of Evolutionary Computation

Publisher: Springer International Publishing

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Abstract

Computational neuroscience lays the foundations of intelligent behavior through the application of machine learning approaches. Brain programming, which derives from such approaches, is emerging as a new evolutionary computing paradigm for solving computer vision and pattern recognition problems. Primate brains have several distinctive features that are obtained by a complex arrangement of highly interconnected and numerous cortical visual areas. This paper describes a virtual system that mimics the complex structure of primate brains composed of an artificial dorsal pathway – or “where” stream – and an artificial ventral pathway – or “what” stream – that are fused to recreate an artificial visual cortex. The goal is to show that brain programming is able to discover numerous heterogeneous functions that are applied within a hierarchical structure of our virtual brain. Thus, the proposal applies two key ideas: first, object recognition can be achieved by a hierarchical structure in combination with the concept of function composition; second, the functions can be discovered through multiple random runs of the search process. This last point is important since is the first step in any evolutionary algorithm; in this way, enhancing the possibilities for solving hard optimization problems.

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previous chapter Feature Selection in High Dimensional Data by a Filter-Based Genetic Algorithm
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Metadata
Title
Brain Programming and the Random Search in Object Categorization
Authors
Gustavo Olague
Eddie Clemente
Daniel E. Hernández
Aaron Barrera
Copyright Year
2017
Book
Applications of Evolutionary Computation

Print ISBN: 978-3-319-55848-6

Electronic ISBN: 978-3-319-55849-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-55849-3_34

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