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The DNA fragment assembly is a very complex optimization problem important within many fields, such as bioinformatics, computational biology or medicine. The problem is NP-hard, that is why many computational techniques, including computational intelligence algorithms, were designed to find good solutions for this problem. This paper is intended to present and investigate two reinforcement learning based models for solving the DNA fragment assembly problem. We provide an experimental comparison of these two models, that will study the obtained performances of the reinforcement learning based approaches, by using different action selection policies, with variable parameters.

2010 Mathematics Subject Classification. 68P15, 68T05.1998 CR Categories and Descriptors. I.2.6[Computing Methodologies]: Artificial Intelligence - Learning; I.2.8[Computing Methodologies]: Problem Solving, Control Methods, and Search - Heuristic methods.

Key words and phrases. Bioinformatics, DNA fragment assembly, reinforcement learning, Q-learning.This paper has been presented at the International Conference KEPT2013: Knowledge Engineering Principles and Techniques, organized by Babeș-Bolyai University, Cluj-Napoca, July 5-7 2013.