Data-driven approaches to empirical discovery
Artificial Intelligence
Handbook of algorithms and data structures: in Pascal and C (2nd ed.)
Handbook of algorithms and data structures: in Pascal and C (2nd ed.)
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic programming using a minimum description length principle
Advances in genetic programming
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
Maple V: learning guide
Parallel genetic programming: a scalable implementation using the transputer network architecture
Advances in genetic programming
Genetic recursive regression for modeling and forecasting real-world chaotic time series
Advances in genetic programming
A guide to MATLAB: for beginners and experienced users
A guide to MATLAB: for beginners and experienced users
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Determining Arguments of Invariant Functional Descriptions
Machine Learning
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A hybrid approach based on MEP and CSP for contour registration
Applied Soft Computing
Parse-matrix evolution for symbolic regression
Engineering Applications of Artificial Intelligence
Better GP benchmarks: community survey results and proposals
Genetic Programming and Evolvable Machines
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This paper describes the use of genetic programming to perform automated discovery of numerical approximation formulae. We present results involving rediscovery of known approximations for Harmonic numbers, discovery of rational polynomial approximations for functions of one or more variables, and refinement of existing approximations through both approximation of their error function and incorporation of the approximation as a program tree in the initial GP population. Evolved rational polynomial approximations are compared to Padé approximations obtained through the Maple symbolic mathematics package. We find that approximations evolved by GP can be superior to Padé approximations given certain tradeoffs between approximation cost and accuracy, and that GP is able to evolve approximations in circumstances where the Padé approximation technique cannot be applied. We conclude that genetic programming is a powerful and effective approach that complements but does not replace existing techniques from numerical analysis.