New hyper-heuristic algorithm for gene fragment assembly

Gene assembly is a technique to construct a gene sequence by referring to gene fragments generated by sequencing machine. The gene fragments are often short and come in large number. As the number of gene fragments increases, the complexity of the problem increases, and this situation produces a wider solution space. To solve the gene assembly problem, the gene fragments need to be arranged in the right order. However, due to the complexity and wide solution space, the accurate solution to this problem is difficult to be found. By looking from the computational perspective, gene assembly problem is considered as nondeterministic-polynomial (NP) problem, where the gene assembly problem can be solved by using metaheuristic algorithms. Metaheuristic algorithms optimize the problem by searching for almost optimal solution. In this research, a hyper-heuristic algorithm is proposed to solve gene assembly problem due to its advantages that overcome the metaheuristic algorithms. This research is conducted based on three objectives. First, to analyze two metaheuristic algorithms, Chemical Reaction Optimization (CRO) and Quantum Inspired Evolutionary Algorithm (QIEA), to solve the problem. Second, a new hyper-heuristic algorithm (QCRO) is developed based on CRO and QIEA. Third, the solutions generated from all three algorithms are evaluated by using statistical analysis. The performance of the algorithms is evaluated by convergence analysis. The similarities of the draft gene sequence generated by the algorithms are analyzed by using Basic Local Alignment Search Tool (BLAST). The findings show that QCRO is competent in finding the right order of the fragments and solving the gene assembly problem. In conclusion, this research presented a new hyper-heuristic algorithm to solve gene fragment assembly problem that is derived from two metaheuristic algorithms. This algorithm is capable of finding the right order of the gene fragments and thus solves the gene assembly problem

Adaptive chemical reaction optimization for global numerical optimization

A newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many optimization problems in both discrete and continuous domains. To alleviate the effort in tuning parameters, this paper reduces the number of optimization parameters in canonical CRO and develops an adaptive scheme to evolve them. Our proposed Adaptive CRO (ACRO) adapts better to different optimization problems. We perform simulations with ACRO on a widely-used benchmark of continuous problems. The simulation results show that ACRO has superior performance over canonical CRO.link_to_OA_fulltex