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Download Embedded Robotics - Thomas Braunl
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21 Genetic Programming Control Parameters Initial population Number of generations Value Description 500 generated with ramp half-and-half method 50 [0..49] Probability of crossover 90% crossover operation is performed on 90% of selected individuals Probability of reproduction 10% copy operation is performed on 10% of the selected individual Probability of mutation 0% mutation is not used here Probability of crossover point being a leaf node 10% possibly extending program depth Probability of crossover point being internal node 90% possibly reducing program depth Maximum tree height 5 maximum allowed program depth Number of evaluations per individual 4 starting individual from different positions in the field Maximum simulation time steps per trial 180 time limit for each individual to reach the ball Table 21.3: Parameter settings Genetic Programming Engine Lisp Program Lisp Parser Population Eyesim Simulator Figure 21.7: Evaluation and simulation procedure Fitness function 320 In order to determine the fitness for an individual program, we let each program run a number of times from different starting positions. Potentially more robust solutions can be achieved by choosing the starting positions and orientations at random. However, it is important to ensure that all individuals in a generation get the same starting distances for a run, otherwise, the fitness val-
