Neural Programming and an Internal Reinforcement Policy

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{teller:1996:npirpSV,

• author = "Astro Teller and Manuela Veloso",
• title = "Neural Programming and an Internal Reinforcement Policy",
• booktitle = "International Conference Simulated Evolution and Learning",
• year = "1996",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, ANN",
• URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AS.ps",
• URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/astroseal/astro/seal.html",
• size = "8 pages",
• abstract = "An important reason for the continued popularity of Artificial Neural Networks (ANNs) in the machine learning community is that the gradient-descent backpropagation procedure gives ANNs a locally optimal change procedure and, in addition, a framework for understanding the ANN learning performance. Genetic programming (GP) is also a successful evolutionary learning technique that provides powerful parameterized primitive constructs. Unlike ANNs, though, GP does not have such a principled procedure for changing parts of the learned system based on its current performance. This paper introduces Neural Programming, a connectionist representation for evolving programs that maintains the benefits of GP. The connectionist model of Neural Programming allows for a regression credit-blame procedure in an evolutionary learning system. We describe a general method for an informed feedback mechanism for Neural Programming, Internal Reinforcement. We introduce an Internal Reinforcement procedure and demonstrate its use through an illustrative experiment.",
• notes = "html version available from http://www.cs.cmu.edu/~astro/ SEAL, PADO bucket-brigade IRNP reach given level of performace in 30% of generations taken by NP",

}

Genetic Programming entries for Astro Teller Manuela Veloso

Citations