Internal reinforcement in a connectionist genetic programming approach

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

@Article{Teller:2000:AI,

• author = "Astro Teller and Manuela Veloso",
• title = "Internal reinforcement in a connectionist genetic programming approach",
• journal = "Artificial Intelligence",
• volume = "120",
• pages = "165--198",
• year = "2000",
• number = "2",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Machine learning, Evolutionary computation, Signal understanding, Internal reinforcement, Neural programming, Bucket brigade",
• URL = "http://www.cs.cmu.edu/~coral/publinks/mmv/AIJ-Astro.pdf",
• broken = "http://www.cs.cmu.edu/~coral/publications/b2hd-AIJ-Astro.html",
• URL = "http://citeseer.ist.psu.edu/41715.html",
• broken = "http://www.sciencedirect.com/science/article/B6TYF-40TY77M-1/1/c54fc0ab842b831a76c9e61e1c1c6b85",
• DOI = "doi:10.1016/S0004-3702(00)00023-0",
• size = "34 pages",
• abstract = "Genetic programming (GP) can learn complex concepts by searching for the target concept through evolution of a population of candidate hypothesis programs. However, unlike some learning techniques, such as Artificial Neural Networks (ANNs), GP does not have a principled procedure for changing parts of a learned structure based on that structure's performance on the training data. GP is missing a clear, locally optimal update procedure, the equivalent of gradient-descent backpropagation for ANNs. This article introduces a new algorithm, internal reinforcement, for defining and using performance feedback on program evolution. This internal reinforcement principled mechanism is developed within a new connectionist representation for evolving parameterised programs, namely neural programming. We present the algorithms for the generation of credit and blame assignment in the process of learning programs using neural programming and internal reinforcement. The article includes a comprehensive overview of genetic programming and empirical experiments that demonstrate the increased learning rate obtained by using our principled program evolution approach.",
• notes = "oai:CiteSeerPSU:558697 broken Oct 2022 http://citeseer.ist.psu.edu/558697.html gives a slightly different version",

}

Genetic Programming entries for Astro Teller Manuela Veloso

Citations