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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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Patent
System and method for reducing patterning variability in integrated circuit manufacturing through mask layout corrections
TL;DR: In this article, a system and method of modifying the mask layout shapes of an integrated circuit layout design to compensate for reticle field location-specific systematic CD variations resulting from mask writing process variations, lens imperfections in lithographic patterning, and photoresist process variations called PLC (Process-optimized Layout Compensation).
Journal ArticleDOI
A machine learning approach to inductive query by examples: an experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing
TL;DR: This article believes these inductive machine learning techniques hold promise for the ability to analyze users' preferred documents, identify users' underlying information needs, and also suggest alternatives for search for database management systems and Internet applications.
Journal ArticleDOI
A review on probabilistic graphical models in evolutionary computation
TL;DR: This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems and gives a survey of Probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compares different methods for probabilism modeling in these algorithms.
Journal ArticleDOI
Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete
Fahid Aslam,Furqan Farooq,Muhammad Nasir Amin,Kaffayatullah Khan,Abdul Waheed,Arslan Akbar,Muhammad Faisal Javed,Rayed Alyousef,Hisham Alabdulijabbar +8 more
TL;DR: The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect and variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work.
Journal ArticleDOI
Evolutionary induction of sparse neural trees
TL;DR: A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle and is successfully applied to the induction of higher order neural trees.
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.