Abstract
Planning is a difficult and fundamental problem of AI.
An alternative solution to traditional planning
techniques is to apply Genetic Programming. As a
program is similar to a plan a Genetic Planner can be
constructed that evolves plans to the plan solution.
One of the stages of the Genetic Programming algorithm
is the initial population seeding stage. We present
five alternatives to simple random selection based on
simple search. We found that some of these strategies
did improve the initial population, and the efficiency
of the Genetic Planner over simple random selection of
actions.
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