Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Languages, behaviors, hybrid architectures, and motion control
Mathematical control theory
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Maintaining the Diversity of Genetic Programs
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Energy aware HW/SW integration in an autonomous microrobot
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
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In this paper we propose a new diversity measure based on the correlation of bit strings for the analysis of Genetic Programming (GP) experiments. The diversity measure has been applied to analyse the impact of pruning on the diversity of a population during genetic programming and it's relation to the convergence time of the fitness function. To show the usability of the proposed diversity measure a GP experiment is introduced where simulated Jasmine robots have to learn a collison avoidance behaviour to find their way through a maze. A full analysis of this experiment is given with different fixed pruning strategies in respect to the population diversity and fitness. The GP has been done on behaviour-based robot controllers implemented in MDL2Ɛ. MDL2Ɛ has the advantage that it provides a very compact bit string representation of the control programme, which can be used for diversity analysis.