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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
Evolving grounded communication for robots
TL;DR: The objective is to come up with precise operational models of how communities of agents, equipped with a cognitive apparatus, a sensori-motor system, and a body, can arrive at shared grounded communication systems.
Journal ArticleDOI
Principles in the Evolutionary Design of Digital Circuits—Part II
TL;DR: It is argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design, which explain how to build systems which are too large to evolve.
Journal ArticleDOI
Evolutionary computation and structural design: A survey of the state-of-the-art
TL;DR: An extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here.
Journal ArticleDOI
Detection of financial statement fraud and feature selection using data mining techniques
TL;DR: Data mining techniques such as Multilayer Feed Forward Neural Network, Support Vector Machines, genetic programming, Genetic Programming, Group Method of Data Handling, Logistic Regression, and Probabilistic Neural Network are used to identify companies that resort to financial statement fraud.
Journal ArticleDOI
Proposed minimum reporting standards for data analysis in metabolomics
Royston Goodacre,David Broadhurst,Age K. Smilde,Bruce S. Kristal,J. David Baker,Richard D. Beger,Conrad Bessant,Susan C. Connor,Giorgio Capuani,Andrew Craig,Timothy M. D. Ebbels,Douglas B. Kell,Cesare Manetti,Jack Newton,Giovanni Paternostro,Ray Somorjai,Michael Sjöström,Johan Trygg,Florian Wulfert +18 more
TL;DR: The goal of this group is to define the reporting requirements associated with the statistical analysis of metabolite data with respect to other measured/collected experimental data (often called meta-data).
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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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.