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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Journal ArticleDOI
Selection Based on the Pareto Nondomination Criterion for Controlling Code Growth in Genetic Programming
Anikó Ekárt,Sandor Nemeth +1 more
TL;DR: It is shown that selection based on the Pareto nondomination criterion reduces code growth and processing time without significant loss of solution accuracy.
Journal ArticleDOI
A survey of teaching–learning-based optimization
Feng Zou,Debao Chen,Qingzheng Xu +2 more
TL;DR: A brief review of the basic concepts of TLBO and a comprehensive survey of its prominent variants and its typical application, and the theoretical analysis conducted on TLBO so far are provided.
Journal ArticleDOI
Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process
TL;DR: A new encoding scheme is presented to genetically design the generalized GMDH-type neural networks in which the connectivity configuration in such networks is not limited to adjacent layers and generalization of network's topology provides optimal networks in terms of hidden layers and or number of neurons.
Journal ArticleDOI
Forecasting container throughputs at ports using genetic programming
Shih-Huang Chen,Jun-Nan Chen +1 more
TL;DR: Results suggest that GP is the optimal method for this case of volumes of container throughput at ports by using genetic programming, decomposition approach, and seasonal auto regression integrated moving average, and SARIMA.
Posted Content
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Brenden K. Petersen,Mikel Landajuela Larma,T. Nathan Mundhenk,Cláudio P. Santiago,Sookyung Kim,Joanne Taery Kim +5 more
TL;DR: The proposed framework uses a recurrent neural network to emit a distribution over tractable mathematical expressions, and employs reinforcement learning to train the network to generate better-fitting expressions, which significantly outperforms standard genetic programming-based symbolic regression in its ability to exactly recover symbolic expressions.
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.