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
Journal ArticleDOI
Discovery of context-specific ranking functions for effective information retrieval using genetic programming
TL;DR: It is argued that the ranking strategy should be context specific and a new systematic method that can automatically generate ranking strategies for different contexts based on genetic programming (GP) is proposed.
Journal ArticleDOI
The invention of CMOS amplifiers using genetic programming and current-flow analysis
T. Sripramong,C. Toumazou +1 more
TL;DR: Experimental results show a promising improvement on the design of operational amplifiers that make the automated analog design environment using genetic programming a lot more practical.
Journal ArticleDOI
Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot
TL;DR: Results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments, which could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments.
Journal ArticleDOI
New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming
TL;DR: In this paper, a new approach for the formulation of compressive strength of carbon fiber reinforced plastic (CFRP) confined concrete cylinders using a promising variant of genetic programming (GP) was proposed.
Journal ArticleDOI
A hybrid computational approach to derive new ground-motion prediction equations
TL;DR: A novel hybrid method coupling genetic programming and orthogonal least squares, called GP/OLS, was employed to derive new ground-motion prediction equations (GMPEs), which are remarkably simple and straightforward and can be used for the pre-design purposes.
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.