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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Journal ArticleDOI
Theory of evolutionary algorithms: a bird's eye view
Agoston E. Eiben,Günter Rudolph +1 more
TL;DR: The most important questions, research topics and technical tools used in various branches of evolutionary algorithms are considered and a road map is given to facilitate the readers’ orientation in evolutionary computation theory.
Proceedings Article
A Case-based Reasoning Approach to Imitating RoboCup Players
TL;DR: An effort to train a RoboCup soccer-playing agent playing in the Simulation League using casebased reasoning, which requires little human intervention and can be used to train agents exhibiting diverse behaviour in an automated manner.
Journal ArticleDOI
An Analysis of the Causes of Code Growth in Genetic Programming
TL;DR: It is shown that single node mutations increase code growth in evolving programs, strong evidence that the protective hypothesis is correct and a negative correlation between the size of the branch removed during crossover and the resulting change in fitness, but a much weaker correlation for added branches.
Journal ArticleDOI
A numerical approach to genetic programming for system identification
TL;DR: This paper introduces a new approach to genetic programming (GP), based on a numerical technique, which integrates a GP-based adaptive search of tree structures, and a local parameter tuning mechanism employing statistical search (a system identification technique).
Automated synthesis and optimization of robot configurations: an evolutionary approach
Patrick C. Leger,John Bares +1 more
TL;DR: This research focuses on the development of synthesis capabilities required for many robot design problems: a flexible and effective synthesis algorithm, useful simulation capabilities, appropriate representation of robots and their properties, and the ability to accomodate application-specific synthesis needs.
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.