Optical NP problem solver on laser-written waveguide platform

Cognitive photonic networks are researched to efficiently solve computationally hard problems. Flexible fabrication techniques for the implementation of such networks into compact and scalable chips are desirable for the study of new optical computing schemes and algorithm optimization. Here we demonstrate a femtosecond laser-written optical oracle based on cascaded directional couplers in glass, for the solution of the Hamiltonian path problem. By interrogating the integrated photonic chip with ultrashort laser pulses, we were able to distinguish the different paths traveled by light pulses, and thus infer the existence or the absence of the Hamiltonian path in the network by using an optical correlator. This work proves that graph theory problems may be easily implemented in integrated photonic networks, down scaling the net size and speeding up execution times

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