IMPROBED: Multiple Problem-Solving Brain via Evolved Developmental Programs

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@Article{Miller:2021:ALife,

• author = "Julian Francis Miller",
• title = "{IMPROBED}: Multiple Problem-Solving Brain via Evolved Developmental Programs",
• journal = "Artificial Life",
• year = "2021",
• volume = "27",
• number = "3-4",
• pages = "300--335",
• month = "Summer-Fall",
• note = "Special issue highlights from the 2020 Conference on Artificial Life",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN, Computational neuro-inspired development, general artificial intelligence, evolutionary algorithms",
• ISSN = "1064-5462",
• URL = "https://doi.org/10.1162/artl_a_00346",
• eprint = "https://direct.mit.edu/artl/article-pdf/27/3%e2%80%934/300/2003291/artl_a_00346.pdf",
• DOI = "doi:10.1162/artl_a_00346",
• abstract = "Artificial neural networks (ANNs) were originally inspired by the brain; however, very few models use evolution and development, both of which are fundamental to the construction of the brain. We describe a simple neural model, called IMPROBED, in which two neural programs construct an artificial brain that can simultaneously solve multiple computational problems. One program represents the neuron soma and the other the dendrite. The soma program decides whether neurons move, change, die, or replicate. The dendrite program decides whether dendrites extend, change, die, or replicate. Since developmental programs build networks that change over time, it is necessary to define new problem classes that are suitable to evaluate such approaches. We show that the pair of evolved programs can build a single network from which multiple conventional ANNs can be extracted, each of which can solve a different computational problem. Our approach is quite general and it could be applied to a much wider variety of problems.",
• notes = "Published March 2022. See also \cite{Wilson:2022:sigevolution}",

}

Genetic Programming entries for Julian F Miller

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