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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
Self-reproduction in cellular automata
TL;DR: It is drawn that although the capacity for universal construction is a sufficient condition for self-reproduction, it is not a necessary condition, and a simple self- reproducing structure is exhibited which satisfies these new criteria.
Book ChapterDOI
The truck backer-upper: an example of self-learning in neural networks
Derrick H. Nguyen,Bernard Widrow +1 more
TL;DR: A two-layer neural network containing 26 adaptive neural elements has learned to back up a computer-simulated trailer truck to a loading dock, even when initially jackknifed.
Journal ArticleDOI
Random numbers fall mainly in the planes
TL;DR: The paper gives details of the degree of regularity of congruential random number generators in terms of sets of relatively few parallel hyperplanes which contain all of the points produced by the generator.
Journal ArticleDOI
Random sequence generation by cellular automata
TL;DR: A 1-dimensional cellular automaton which generates random sequences is discussed, and an efficient random sequence generator based on them is suggested.