By Shu-Heng Chen (auth.), Shu-Heng Chen (eds.)
After a decade of improvement, genetic algorithms and genetic programming became a extensively accredited toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering quantity committed fullyyt to a scientific and accomplished overview of this topic. Chapters hide numerous components of computational finance, together with monetary forecasting, buying and selling techniques improvement, money movement administration, choice pricing, portfolio administration, volatility modeling, arbitraging, and agent-based simulations of man-made inventory markets. instructional chapters also are integrated to assist readers speedy grab the essence of those instruments. eventually, a menu-driven software, basic GP, accompanies the amount, so as to let readers and not using a powerful programming history to achieve hands-on adventure in facing a lot of the technical fabric brought during this work.
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Mutation is performed with low probability. 44 Inversion. Another operator in Holland's genetic algorithms was the inversion operator. Inversion is a process that shifts the locus of one or more genes in a chromosome from one point to another. This does not change the meaning of the genotype, in that a genotype before and after inversion will still decode to the same phenotype. This is the same as in natural genetics, where the function of a gene is independent of its position in the chromosome.
Holland, B. LeBaron, and P. Tayler (1994). "Artificial Economic Life: A Simple Model of a Stockmarket," Physica D, 75, 264-274. Pereira, R. (1996). "Selecting Parameters for Technical Trading Rules Using Genetic Algorithms," Journal of Applied Finance and Investment, 1(3), July/August 27-34. Pereira, R. (2002). -H. ), Evolutionary Computation in Economics and Finance, 287-309. Physica-Verlag. 26 GA AND GP IN COMPUTATIONAL FINANCE Riechmann, T. (1999). "Learning and Behavioural Stability: An Economic Interpretation of Genetic Algorithms," Journal of Evolutionary Economics, 9(2), 225-242.
Notes 1. See Chen and Kuo (2002) pp. 425-426 for details. 2. These results can be compared to the main finding in Kaboudan (2000), which shows that one should use price series rather than return series to forecast the price. 3. There is, however, another approach to enhance the flexibility of Bauer's trading strategies. That is, to parameterize trading rules, and then encode them with bit strings. The GA is then used to evolve these strings. Examples can be found in Pereira (2002). 4. The combinatoric approach was also frequently seen is other application domains.