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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On-Chip Communication Architectures: System on Chip Interconnect
Sudeep Pasricha,Nikil Dutt +1 more
TL;DR: This book is a comprehensive reference on concepts, research and trends in on-chip communication architecture design, and will provide readers with a comprehensive survey, not available elsewhere, of all current standards for on- chip communication architectures.
Journal ArticleDOI
Modeling Design Exploration as Co-Evolution
Mary Lou Maher,Josiah Poon +1 more
TL;DR: A model for problem-design exploration, and how this model can be implemented using the genetic algorithm (GA) paradigm, in which the fitness function is represented within the genotype and modelled as a separately evolving population of genotypes.
Journal ArticleDOI
Applications of neural networks to digital communications: a survey
TL;DR: An overview of the applications of NNs to digital communications such as channel identification and equalization, coding and decoding, vector quantization, image processing, nonlinear filtering, spread spectrum applications, etc is given.
Proceedings ArticleDOI
Multiobjective genetic programming: reducing bloat using SPEA2
TL;DR: This study investigates the use of multiobjective techniques in genetic programming in order to evolve compact programs and to reduce the effects caused by bloating with regard to both convergence speed and size of the produced programs on an even-parity problem.
BookDOI
Search Based Software Engineering
Günther Ruhe,Yuanyuan Zhang +1 more
TL;DR: This keynote address surveys recent success and momentum in the subfield of automated program repair and argues for the importance of human studies in automated software engineering and challenges the SBSE community to facilitate reproducible research and scientific advancement through benchmark creation.
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.