Crossover context in page-based linear genetic programming
This work explores strategy learning through genetic programming (GP) in artificial "ants" that navigate the San Mateo Trail. Several properties of linearly structured (as opposed to typical am-based) GP are investigated, including the significance of simple register-based memories, the significance of constraints applied to the crossover operator, and the significance of the ant's level of activity. A basis for investigating more thoroughly the relation between specific code sequences and fitness is provided by dividing the genome into pages of instructions and introducing an analysis of fitness change and exploration of the trail done by particular parts of a genome. By doing so it becomes possible to present results on how best to find the instructions in an individual's program that contribute positively to the accumulation of effective search strategies.
genetic programming strategy learning