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
Advances in genetic programming
The Application of Genetic Programming to the Investigation of Short, Noisy, Chaotic Data Series
Selected Papers from AISB Workshop on Evolutionary Computing
Genetic Programming Prediction of Stock Prices
Computational Economics
Exhaustive search for perfect predictors in complex binary data
NOLASC'05 Proceedings of the 4th WSEAS International Conference on Non-linear Analysis, Non-linear Systems and Chaos
A bootstrapping approach to reduce over-fitting in genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This study assessed the use of genetic programming (GP) to diagnose chaos. Fifty GP runs were performed on chaotic data, generated from the Mackey-Glass delay differential equation, on one surrogate with the same Fourier power spectrum and statistics but without chaotic dynamics, and on a random walk series. Single runs were performed on 50 different surrogates of the chaotic series. Fitness was measured across 5 separate forecast periods of 65 points each, each based upon 10 prior input data points. Fittest program fragments for the chaotic series evolved later and were more complicated than those for the surrogates. Relative to fitnesses achieved by constant linear predictions, fitnesses from the chaotic series were also better. Random walk data resulted in an impoverished GP process, with quick evolution of simple program fragments but no later evolutionary improvement. This comparative test merits assessment on other datasets, and its implications with respect to the statistical bootstrap and GP estimation are discussed.