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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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Proceedings ArticleDOI
Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)
TL;DR: A novel evolutionary method named Genetic Network Programming (GNP), whose genome is a network structure is proposed to overcome the low searching efficiency of GP and is applied to the problem of the evolution of ant behavior in order to study the effectiveness of GNP.
Journal ArticleDOI
Financial forecasting using genetic algorithms
S. Mahfoud,Ganesh Mani +1 more
TL;DR: A new genetic algorithm based system is presented and applied to the task of predicting the future performances of individual stocks and is compared to an established neural network system in the domain of financial fore casting.
Journal ArticleDOI
Genetic programming for the identification of nonlinear input-output models
TL;DR: The main idea of the paper is to apply the orthogonal least squares (OLS) algorithm to estimate the contribution of the branches of the tree to the accuracy of the model, which results in more robust and interpretable models.
Journal ArticleDOI
Nonlinear model structure identification using genetic programming
TL;DR: In this article, genetic programming is applied to the identification of the nonlinear structure of a dynamic model from experimental data, where the model structure may be described either by differential equations or by a block diagram.
Journal ArticleDOI
Effects of code growth and parsimony pressure on populations in genetic programming
Terence Soule,James A. Foster +1 more
TL;DR: In this article, the effect of parsimony pressure can be measured by calculating the relationship between program size and performance within the population, which can be used as a partial indicator of success or failure for individual populations.
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.
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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.
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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.