Vertical decomposition with Genetic Algorithm for Multiple Sequence Alignment

Many Bioinformatics studies begin with a multiple sequence alignment as the foundation for their research. This is because multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence structure relationships.In this paper, we have proposed a Vertical Decomposition with Genetic Algorithm (VDGA) for Multiple Sequence Alignment (MSA). In VDGA, we divide the sequences vertically into two or more subsequences, and then solve them individually using a guide tree approach. Finally, we combine all the subsequences to generate a new multiple sequence alignment. This technique is applied on the solutions of the initial generation and of each child generation within VDGA. We have used two mechanisms to generate an initial population in this research: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with VDGA. To test the performance of our algorithm, we have compared it with existing well-known methods, namely PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on Genetic Algorithms (GA), such as SAGA, MSA-GA and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0.The experimental results showed that the VDGA with three vertical divisions was the most successful variant for most of the test cases in comparison to other divisions considered with VDGA. The experimental results also confirmed that VDGA outperformed the other methods considered in this research

Fitness inheritance for noisy evolutionary multi-objective optimization

This paper compares the performance of anti-noise methods, particularly probabilistic and re-sampling methods, using NSGA2. It then proposes a computationally less expensive approach to counteracting noise using re-sampling and fitness inheritance. Six problems with different difficulties are used to test the methods. The results indicate that the probabilistic approach has better convergence to the Pareto optimal front, but it looses diversity quickly. However, methods based on re-sampling are more robust against noise but they are computationally very expensive to use. The proposed fitness inheritance approach is very competitive to re-sampling methods with much lower computational cost

Multiple-order permutation flow shop scheduling under process interruptions

© 2018, Springer-Verlag London Ltd., part of Springer Nature. The permutation flow shop problem is a complex combinatorial optimization problem. Over the last few decades, a good number of algorithms have been proposed to solve static permutation flow shop problems. However, in practice, permutation flow shop problems are not static but rather are dynamic because the orders (where each order contains multiple jobs) arrive randomly for processing and the operation of any job may be interrupted due to resource problems. For any interruption, it is necessary to reschedule the existing jobs that are under process at different stages in the production system and also any orders that were previously accepted that are waiting for processing. In this paper, a memetic algorithm-based rescheduling approach has been proposed to deal with both single and multiple orders while considering random interruptions of resources. The experimental results have shown that the performance of the proposed approach is superior to traditional reactive approaches

A methodology for the large-scale multi-period precedence-constrained knapsack problem: an application in the mining industry

This paper considers a generalization of the precedence-constrained knapsack problem known as multi-period precedence-constrained knapsack, in which the decision maker faces a horizon of several periods. Associated with each period is a capacity limit that cannot be exceeded by items chosen in that specific period. The motivation for studying this problem comes from a recognized problem in the mining industry, known as open pit mine production scheduling. An old, yet fast sequencing heuristic has been used in the literature to tackle similar combinatorial problems with precedence constraints. In this study, we first strengthen the LP relaxation formulation of the problem by adding inequalities derived from both precedence and knapsack constraints, and then use the LP solutions to generate efficient weights for the sequencing heuristic. Generating the heuristic's weights in this way significantly improves its output. Using this methodology, extremely large instances can be solved to near-optimum levels in minutes. The performance of this methodology is tested on a set of benchmark instances in the mining industry, where this problem is a major application