Pedro Pinacho-Davidson

Christian Blum, Pedro Pinacho-Davidson

The longest run subsequence (LRS) problem is an NP-hard combinatorial optimization problem belonging to the class of subsequence problems from bioinformatics. In particular, the problem plays a role in genome reassembly. In this paper, we present a solution to the LRS problem using a Biased Random Key Genetic Algorithm (BRKGA). Our approach places particular focus on the computational efficiency of evaluating individuals, which involves converting vectors of gray values into valid solutions to the problem. For comparison purposes, a Max-Min Ant System is developed and implemented. This is in addition to the application of the integer linear programming solver CPLEX for solving all considered problem instances. The computation results show that the proposed BRKGA is currently a state-of-the-art technique for the LRS problem. Nevertheless, the results also show that there is room for improvement, especially in the context of input strings based on large alphabet sizes.

Pedro Pinacho-Davidson, Matías Lermanda, Ricardo Contreras et al.

The detection of anomalies in unknown environments is a problem that has been approached from different perspectives with variable results. Artificial Immune Systems (AIS) present particularly advantageous characteristics for the detection of such anomalies. This research is based on an existing detector model, named Artificial Bioindicators System (ABS) which identifies and solves its main weaknesses. An ABS based anomaly classifier model is presented, incorporating elements of the AIS. In this way, a new model (R-ABS) is developed which includes the advantageous capabilities of an ABS plus the reactive capabilities of an AIS to overcome its weaknesses and disadvantages. The RABS model was tested using the well-known DARPA'98 dataset, plus a dataset built to carry out a greater number of experiments. The performance of the RABS model was compared to the performance of the ABS model based on classical sensitivity and specificity metrics, plus a response time metric to illustrate the rapid response of R-ABS relative to ABS. The results showed a better performance of R-ABS, especially in terms of detection time.

Christian Blum, José A. Lozano, Pedro Pinacho Davidson

The minimum common string partition problem is an NP-hard combinatorial optimization problem with applications in computational biology. In this work we propose the first integer linear programming model for solving this problem. Moreover, on the basis of the integer linear programming model we develop a deterministic 2-phase heuristic which is applicable to larger problem instances. The results show that provenly optimal solutions can be obtained for problem instances of small and medium size from the literature by solving the proposed integer linear programming model with CPLEX. Furthermore, new best-known solutions are obtained for all considered problem instances from the literature. Concerning the heuristic, we were able to show that it outperforms heuristic competitors from the related literature.