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
Open issues in genetic programming
TL;DR: Some of the challenges and open issues that face researchers and practitioners of GP are outlined and it is hoped this overview will stimulate debate, focus the direction of future research to deepen the understanding of GP, and further the development of more powerful problem solving algorithms.
Journal ArticleDOI
Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data
TL;DR: Experimental results on six (binary) class imbalance problems show that the evolved ensembles outperform their individual members, as well as single-predictor methods such as canonical GP, naive Bayes, and support vector machines, on highly unbalanced tasks.
Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work
Melanie Mitchell,James P. Crutch +1 more
TL;DR: The work described here is the first step in employing GAs to engineer useful emergent computation in decentralized multi-processor systems and is also a step in understanding how an evolutionary process can produce complex systems with sophisticated collective computational abilities.
Book
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence
TL;DR: This chapter is not meant to be an exhaustive primer on linear control theory, although key concepts from optimal control are introduced as needed to build intuition and demonstrate known optimal solutions to linear control problems.
Journal ArticleDOI
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Amir H. Gandomi,Amir H. Alavi +1 more
TL;DR: The proposed MSGP-based solutions are capable of effectively simulating the nonlinear behavior of the investigated systems and are found to be more accurate than those of standard GP and artificial neural network-based models.
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.