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
More filters
Patent
Method and apparatus for automated design of complex structures using genetic programming
TL;DR: In this article, an automated design process and apparatus for use in designing complex structures, such as circuits, to satisfy prespecified design goals, using genetic operations, was presented, using a population of entities which may be evolved to generate structures (1024-1028) that may potentially satisfy the design goals.
Book
Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence
TL;DR: This paper presents a meta-anatomy of evolutionary algorithms and some examples of successful and unsuccessful attempts at optimization in the context of discrete-time programming.
Journal ArticleDOI
Formulation of flow number of asphalt mixes using a hybrid computational method
TL;DR: In this paper, a high-precision model was derived to predict the flow number of dense asphalt mixtures using a hybrid method coupling genetic programming and simulated annealing, called GP/SA.
Book
Genetic programming using a minimum description length principle
TL;DR: This paper introduces a method for controlling tree growth, which uses an MDL principle, and shows how MDL-based tness functions can be applied successfully to problems of pattern recognitions.
Journal ArticleDOI
Genetic Programming Approach for Prediction of Local Scour Downstream of Hydraulic Structures
Aytac Guven,Mustafa Gunal +1 more
TL;DR: In this paper, genetic programming (GP) is used for predicting local scour downstream of grade control structures. But the training and testing patterns of the proposed GP formulation are based on well established and widely dispersed experimental results from the literature.
References
More filters
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.