Abstract
This work explores strategy learning through genetic
programming in artificial 'ants' that navigate the San
Mateo trail. We investigate several properties of
linearly structured (as opposed to typical tree-based)
GP including: the significance of simple register based
memories, the significance of constraints applied to
the crossover operator, and how 'active' the ant are.
We also provide a basis for investigating more
thoroughly the relation between specific code sequences
and fitness 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 we are able 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.
- (artificial
- algorithms,
- ants,
- artificial
- based
- change,
- code
- crossover
- effective
- fitness
- genetic
- instructions,
- intelligence),
- learning
- learning,
- mateo
- memories,
- operator,
- problems,
- programming,
- register
- san
- search
- sequences,
- simple
- strategies,
- strategy
- trail,
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