Abstract

Genetic Programming (GP) is an automated computational programming methodology, inspired by the workings of natural evolution techniques. It has been applied to solve complex problems in multiple domains including finance. This paper illustrates the application of an adaptive form of GP, where the probability of crossover and mutation is adapted dynamically during the GP run, to the important real-world problem of options pricing. The tests are carried out using market option price data and the results illustrate that the new method yields better results than are obtained from GP with fixed crossover and mutation rates. The developed method has potential for implementation across a range of dynamic problem environments. Categories and Subject Descriptors

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