DNA Fragment Assembly Using a Teaching–Learning-based Optimization Algorithm

Abstract

In this work, we propose an adaptation of the novel Teaching–Learning-Based Optimization (TLBO) algorithm for assembling DNA fragments. TLBO is a population-based metaheuristic that simulates the classical school learning process. The TLBO algorithm implemented in this work operates in two stages. During the first stage (TeacherPhase), a Probabilistic Edge Recombination Crossover (PERX) operator is applied to build a new solution from three permutation vectors (orders of fragments ID): the current solution, the best individual in the population, and a mean permutation vector calculated from all individuals. During the second stage (LearnerPhase), a PERX operator is applied to the current solution and a randomly selected individual for building two new solutions. In both stages, the new solutions will be only accepted into the population if they are better than the current solution. The algorithm continues until the termination condition is met. Our simulation results on literature benchmarks show that our PERX-based TLBO algorithm, in its basic form, has a very good performance and outperforms our basic discrete PSO algorithm proposed in [1] يتمثل هذا العمل في مطابقة خوارزمية جديدة تسمى بالإنجليزية Teaching–Learning-Based Optimization (TLBO) علىمسألة تجميع قطع الحمض النووي، مسألة تحسين صعبة في مجال المعلوماتية-البيولوجية. هذه الخوارزمية مستوحاة من طريقة نشر المعرفة داخل حجرة الدراسة، حيث يكتسب التلاميذ المعرفة من معلم في البداية، ثم من زملائهم في الصف