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
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Journal ArticleDOI
A smart itsy bitsy spider for the web
TL;DR: This research developed two Web personal spiders based on best first search and genetic algorithm techniques, respectively, and found the Java-based interface to be a necessary component for design of a truly interactive and dynamic Web agent.
Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directionsand Research Directions
TL;DR: In this paper, the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system is explored.
Journal ArticleDOI
Tuning the parameters of an artificial neural network using central composite design and genetic algorithm
TL;DR: The designed ANN, according to the proposed procedure, has a better performance than other networks by random selected parameters and also parameters which are selected by the Taguchi method and can be used for tuning neural network parameters in solving other problems.
Journal ArticleDOI
Technical trading rules in the European Monetary System
TL;DR: This paper used genetic programming to find trading rules that generate significant excess returns for three of four EMS exchange rates over the out-of-sample period 1986-1996, and found no evidence that the excess returns are compensation for bearing systematic risk.
Journal ArticleDOI
Intelligent Computing Methods for Manufacturing Systems
TL;DR: Current trends in applications of intelligent computing tools to manufacturing are discussed and the motivation and basis for the utilisation of these systems are reviewed.
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.