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Publication Detail
Better Trained Ants for Genetic Programming
The problem of programming an artificial ant to follow the Santa Fe trail has been repeatedly used as a benchmark problem in GP. Recently we have shown performance of several techniques is not much better than the best performance obtainable using uniform random search. We suggested that this could be because the program fitness landscape is difficult for hill climbers and the problem is also difficult for Genetic Algorithms as it contains multiple levels of deception. Here we redefine the problem so the ant is (1) obliged to traverse the trail in approximately the correct order, (2) to find food quickly. We also investigate (3) including the ant’s speed in the fitness function, either as a linear addition or as a second objective in a multi-objective fitness function, and (4) GP one point crossover. A simple genetic programming system, with no size or depth restriction, is shown to perform approximately three times better with the improved training function. (Extends CSRP-98-08 \citelangdon:1998:antlook)
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