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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Introduction to Algorithms
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References
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Journal ArticleDOI
The Art of Computer Programming. Volume 2: Seminumerical Algorithms (Donald E. Knuth)
Journal ArticleDOI
A learning machine: part II
TL;DR: It is proposed that the program of a stored-program computer be gradually improved by a learning procedure which tries many programs and chooses, from the instructions that may occupy a given location, the one most often associated with a successful result.
Proceedings Article
Hierarchical genetic algorithms operating on populations of computer programs
TL;DR: A new approach in which the size and shape of the solution to such problems is dynamically created using Darwinian principles of reproduction and survival of the fittest is reported on.
Book ChapterDOI
Learning by experimentation: acquiring and refining problem-solving heuristics
TL;DR: This chapter focuses on the issue of learning heuristics to guide a forward-search problem solver, and describes a computer program called lex, which acquires problem-solving Heuristics in the domain of symbolic integration.
Book
Readings in Machine Learning
TL;DR: Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn.