Open AccessBook
Genetic Programming: On the Programming of Computers by Means of Natural Selection
TLDR
This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.Abstract:
Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repositoryread more
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References
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Journal ArticleDOI
Random number generations generators on Vector supercomputers and other advanced architectures
TL;DR: This paper is divided into three parts: the more common techniques for generating and testing uniformly distributed random numbers, and the efficient methods for calculating these techniques.
Proceedings Article
Using the genetic algorithm to generate LISP source code to solve the prisoner's dilemma
Cory Fujiko,John Dickinson +1 more
TL;DR: In this article, a genetic algorithm is adapted to manipulate Lisp S-expressions and the traditional genetic operators of crossover, inversion, and mutation are modified for the Lisp domain.
Journal ArticleDOI
Extracting cellular automaton rules directly from experimental data
TL;DR: A learning algorithm is employed, the genetic algorithm, to search efficiently through a space of probabilistic CA rules for a local rule that best reproduces the observed behavior of the data.
Book
A pattern-recognition program that generates, evaluates, and adjusts its own operators
Leonard Uhr,Charles Vossler +1 more
TL;DR: This paper describes an attempt to make use of machine learning or self-organizing processes in the design of a pattern-recognition program that learns or constructs a secondary code appropriate for the analysis of the particular set of patterns input to it.