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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Diversity in Neural Network Ensembles
TL;DR: Negative Correlation Learning is found to be a competitive technique, worthy of further application, and one part of it can be analytically determined for any ensemble architecture, and an upper bound on the remaining part of the parameter is defined, indicating that the optimal parameter can be determined exactly.
Book
Adaptive Agents and Multi-Agent Systems II
TL;DR: This work presents several collaboration strategies for agents that learn and their empirical results in several experiments, and analyzes the strategies along several dimensions, like number of agents, redundancy, CBR technique used, and individual decision policies.
Journal ArticleDOI
Evolutionary computing for knowledge discovery in medical diagnosis
TL;DR: Simulation results show that the evolutionary classifier produces comprehensible rules and good classification accuracy for the medical datasets and results obtained from t-tests further justify its robustness and invariance to random partition of datasets.
Competition, Coevolution and the Game of Tag
TL;DR: In the experiments described here, control programs for mobile agents (simulated vehicles) are evolved based on their skill at the game of tag, guided by competitive fitness.
Journal ArticleDOI
Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming
TL;DR: In this paper, a hybrid evolutionary modeling algorithm is presented to implement the automatic modeling of one-and multi-dimensional dynamic systems, where the main idea of the method is to embed a genetic algorithm in genetic programming where the latter algorithm is employed to discover and optimize the structure of a model, while the former algorithm optimizes its parameters.
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.