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
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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An Introduction to Genetic Algorithms
TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Book ChapterDOI
Introduction to Algorithms
TL;DR: This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation.
References
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Proceedings Article
The dynamics of collective sorting robot-like ants and ant-like robots
Jean-Louis Deneubourg,Simon Goss,Nigel R. Franks,Ana B. Sendova-Franks,Claire Detrain,L. Chrétien +5 more
TL;DR: A distributed sorting algorithm, inspired by how ant colonies sort their brood, is presented for use by robot teams, offering the advantages of simplicity, flexibility and robustness.
Book
Pattern Recognition: Statistical, Structural and Neural Approaches
TL;DR: This chapter discusses supervised learning using Parametric and Nonparametric Approaches and unsupervised Learning in NeurPR, and discusses feedforward Networks and Training by Backpropagation.
Book
Models of Thought
TL;DR: The history of cognitive psychology can be traced back to the early 1970s, when Simon and his colleagues were in the front line of the information-processing revolution as mentioned in this paper, and their contributions have written the history of that revolution in cognitive psychology.