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
Citations
More filters
Journal ArticleDOI
Permanent deformation analysis of asphalt mixtures using soft computing techniques
TL;DR: Two branches of soft computing techniques, namely multi expression programming (MEP) and multilayer perceptron (MLP) of artificial neural networks for the evaluation of rutting potential of dense asphalt-aggregate mixtures are presented.
Journal ArticleDOI
Journeys in non-classical computation I: A grand challenge for computing research
Susan Stepney,Samuel L. Braunstein,John A. Clark,Andy M. Tyrrell,Andrew Adamatzky,Robert E. Smith,Tom Addis,Colin G. Johnson,Jonathan Timmis,Peter H. Welch,Robin Milner,Derek Partridge +11 more
TL;DR: The Grand Challenge for computer science is to journey through the gateway event obtained by breaking the authors' current classicalcomputational assumptions, and thereby develop a mature science of Non-ClassicalComputation.
Journal ArticleDOI
MedGA: A novel evolutionary method for image enhancement in medical imaging systems
Leonardo Rundo,Leonardo Rundo,Andrea Tangherloni,Marco S. Nobile,Carmelo Militello,Daniela Besozzi,Giancarlo Mauri,Paolo Cazzaniga +7 more
TL;DR: This work introduces MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions.
Journal ArticleDOI
Soft-Tempering Deep Belief Networks Parameters Through Genetic Programming
span,João P. Papa< sup> +1 more
TL;DR: In this paper, an evolutionary-inspired optimization, known as Genetic Programming, was proposed for Deep Belief Networks hyperparameter selection, where the terminal nodes encoded the hyperparameters of the model, and proper function nodes allowed an excellent combination of mathematical operators.
Journal ArticleDOI
Modelling of explosive cutting process of plates using GMDH-type neural network and singular value decomposition
TL;DR: In this paper, singular value decomposition (SVD) is used to find the vector of coefficients of a quadratic sub-expression embodied in a group method of data handling (GMDH) type neural networks.
References
More filters
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.