Created by W.Langdon from gp-bibliography.bib Revision:1.4221

@TechReport{schmidhuber:2002:TR12, author = "Juergen Schmidhuber", title = "Optimal Ordered Problem Solver", institution = "IDSIA", year = "2002", number = "IDSIA-12-02", month = "31 " # jul, keywords = "genetic algorithms, genetic programming, OOPS, bias-optimality, incremental optimal universal search, metasearching, metalearning, self-improvement", URL = "ftp://ftp.idsia.ch/pub/juergen/oops.ps.gz", abstract = "We extend principles of non-incremental universal search to build a novel, optimally fast, incremental learner that is able to improve itself through experience. The Optimal Ordered Problem Solver (OOPS) searches for a universal algorithm that solves each task in a sequence of tasks. It continually organises and exploits previously found solutions to earlier tasks, efficiently searching not only the space of domain-specific algorithms, but also the space of search algorithms. The initial bias is embodied by a task-dependent probability distribution on possible program prefixes (pieces of code that may continue). Prefixes are self-delimiting and executed in online fashion while being generated. They compute the probabilities of their own possible continuations. Let p^n denote a found prefix solving the first n tasks. It may exploit previous solutions p^i (iGenetic Programming entries for Jurgen Schmidhuber