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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
An application of artificial intelligence for rainfall-runoff modeling
Ali Aytek,M. Asce,Murat Alp +2 more
TL;DR: In this paper, an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC).
Journal ArticleDOI
The development of algebraic stress models using a novel evolutionary algorithm
TL;DR: This study presents one of the most rigorous and complete CFD validation of machine learnt turbulent stress models to date.
Evolutionary algorithms with local search for combinatorial optimization
Mark Land,Richard K. Belew +1 more
TL;DR: It is shown that Darwinian evolution is as effective as Lamarckian, and is more robust under changes to the genetic operators, and the effectiveness of steady-state vs. generational EAs, different local search lengths, and various local/global ratios is explored.
Proceedings ArticleDOI
Scalability problems of digital circuit evolution evolvability and efficient designs
V.K. Vassilev,J.E. Miller +1 more
TL;DR: This paper studies the evolutionary design of combinational circuits, particularly the three-bit multiplier circuit, in which the basic building blocks are small sub-circuits, modules inferred from other evolved designs, and it is shown that in general the principles of evolving digital circuits are scalable.
Journal Article
XCS and GALE: A comparative study of two learning classifier systems on data mining
TL;DR: This paper compares the learning performance, in terms of prediction accuracy, of two genetic-based learning systems, XCS and GALE, with six well-known learning algorithms, coming from instance based learning, decision tree induction, rule-learning, statistical modeling and support vector machines.
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.
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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.