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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
Citations
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Journal ArticleDOI
On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis
TL;DR: In this article, the authors apply a series of econometric tests to show that the efficient market hypothesis and the rational expectations hypothesis can be satisfied with some portions of the artificial time series.
Journal ArticleDOI
Steady-state modelling of chemical process systems using genetic programming
TL;DR: Genetic Programming is proposed as a method for developing input-output process models from experimental data that can discriminate between relevant and irrelevant process inputs, yielding parsimonious model structures that accurately represent process characteristics.
Journal ArticleDOI
Fault detection using genetic programming
TL;DR: Genetic programming is used to detect faults in rotating machinery to examine the performance of two-class normal/fault recognition and the results are compared with a few other methods for fault detection.
Proceedings Article
Faster genetic programming based on local gradient search of numeric leaf values
TL;DR: This work examines the effectiveness of gradient search optimization of numeric leaf values for Genetic Programming and shows how conventional random constants are tuned by gradient descent with minimal overhead.
Journal ArticleDOI
Automated discovery of local search heuristics for satisfiability testing
TL;DR: It is shown that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT, and the local search behavior of the learnedHeuristics is analyzed using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Ecological Diversity and its Measurement
TL;DR: In this paper, the authors define definitions of diversity and apply them to the problem of measuring species diversity, choosing an index and interpreting diversity measures, and applying them to structural and structural diversity.
Book
The perception: a probabilistic model for information storage and organization in the brain
TL;DR: The second and third questions are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory as mentioned in this paper.