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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Proceedings Article
Automatic design of balanced board games
Vincent Hom,Joe Marks +1 more
TL;DR: This paper describes a first attempt at using AI techniques to design balanced board games like checkers and Go by modifying the rules of the game, not just the rule parameters.
Journal ArticleDOI
Using Genetic Search for Reverse Engineering of Parametric Behavior Models for Performance Prediction
TL;DR: This paper presents a novel comprehensive approach for reverse engineering and performance prediction of components using genetic programming for reconstructing a behavior model from monitoring data, runtime bytecode counts, and static bytecode analysis.
Journal ArticleDOI
Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming
TL;DR: Two soft computing approaches, which are known as artificial neural networks and Gene Expression Programming are used in strength prediction of basalts which are collected from Gaziantep region in Turkey and it is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts.
Autoconstructive Evolution: Push, PushGP, and Pushpop
TL;DR: This paper is a preliminary report on autoconstructive evolution, a framework for evolutionary computation in which the machinery of reproduction and diversification evolves within the individuals of an evolving population of problem solvers.
Mason: a java multi-agent simulation library
TL;DR: MASON (Multi-Agent Simulator Of Neighborhoods) is intended to provide a core of facilities useful not only to social science but to other agent-based modeling fields such as artificial intelligence and robotics, and can foster useful “cross-pollination” between such diverse disciplines.
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.