Abstract
In this chapter, we show the real-world applications of genetic programming (GP) to bioinformatics and robotics. In the bioinformatics application, we propose majority voting technique for the prediction of the class of a test sample. In the application to robotics, we use GP to generate the motion sequences of humanoid robots. We introduce an integrated approach, i.e., the combination of GP and reinforcement learning, to design the desirable motions. The effectiveness of our proposed approaches is demonstrated by performing experiments with real data, i.e., classifying real micro-array gene expression profiles and controlling real humanoid robots.
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Paul, T.K., Iba, H. (2007). Genetic Programming for Classifying Cancer Data and Controlling Humanoid Robots. In: Riolo, R., Soule, T., Worzel, B. (eds) Genetic Programming Theory and Practice IV. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-49650-4_4
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DOI: https://doi.org/10.1007/978-0-387-49650-4_4
Publisher Name: Springer, Boston, MA
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