A COMPARISON OF REINFORCEMENT LEARNING BASED MODELS FOR THE DNA FRAGMENT ASSEMBLY PROBLEM

The DNA fragment assembly is a very complex optimiza- tion problem important within many elds, such as bioinformatics, com- putational biology or medicine. The problem is NP-hard, that is why many computational techniques, including computational intelligence al- gorithms, were designed to nd 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 pro- vide an experimental comparison of these two models, that will study the obtained performances of the reinforcement learning based approaches, by using dierent action selection policies, with variable parameters.

[1]  Long Ji Lin,et al.  Self-improving reactive agents based on reinforcement learning, planning and teaching , 1992, Machine Learning.

[2]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[3]  Bruno Apolloni,et al.  DNA Fragment Assembly Using Neural Prediction Techniques , 1999, Int. J. Neural Syst..

[4]  Gabriela Czibula,et al.  Temporal Ordering of Cancer Microarray Data through a Reinforcement Learning Based Approach , 2013, PloS one.

[5]  Ajith Abraham,et al.  Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges , 2008, Computational Intelligence in Biomedicine and Bioinformatics.

[6]  Maria-Iuliana Bocicor,et al.  A Reinforcement Learning Approach for Solving the Fragment Assembly Problem , 2011, 2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

[7]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[8]  Goutam Chakraborty,et al.  Heuristically Tuned GA to Solve Genome Fragment Assembly Problem , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  Sebastian Thrun,et al.  The role of exploration in learning control , 1992 .

[10]  Stephanie Forrest,et al.  Genetic algorithms, operators, and DNA fragment assembly , 1995, Machine Learning.

[11]  Sami Khuri,et al.  A Comparison of DNA Fragment Assembly Algorithms , 2004, METMBS.

[12]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[13]  Adrian Filipescu,et al.  Real-time control of autonomous mobile robots using virtual pheromones , 2009, 2009 7th Asian Control Conference.