Zusammenfassung
Genetic Programming (GP) is an automated computational
programming methodology which is inspired by the
workings of natural evolution techniques. It has been
applied to solve complex problems in multiple
application domains. This paper investigates the
application of a dynamic form of GP in which the
probability of crossover and mutation adapts during the
GP run. This allows GP to adapt its
diversity-generating process during a run in response
to feedback from the fitness function. A proof of
concept study is then undertaken on the important
real-world problem of options pricing. The results
indicate that the dynamic form of GP yields better
results than are obtained from canonical GP with fixed
crossover and mutation rates. The developed method has
potential for implementation across a range of dynamic
problem environments.
Nutzer