I have a few questions about linear genetic programming. I'm struggling to find much information on them, hopefully someone here can help me, it would be much appreciated.
1) Initialisation: When initialising the population, does each individual point to the same set of registers or register values, or is a separate space allocated to each individual ?
2) Crossover / mutation: Are the registers crossed over and mutated as well as the program bodies, or just the program bodies ? I am guessing they're not, but then how would one go about optimising the values in the registers ?
3) Crossover cont: What, based on your experience, is the most efficient method of crossover ? I have written a linear GP, and have applied to various datasets for time series forecasting. One being linear, a few trigonometric, and some economic data. It works fine when given anything except the economic data. Why could this be ? My guess is that is has something to do with crossover, which at present is two point. Although i have tested other methods such as one point and uniform but they have not in any way improved the performance? The first few generations are ok, and then the fitness of the population seems to fluctuate randomly with no improvement whatsoever :(
These may seem like very simple, or strange questions. I have only just begun learning about this so please be nice ! :)