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Recently, the identification of biological neural networks has been reformulated as an optimization problem based on a framework of adaptive synchronization. In this paper, four different optimization algorithms, including genetic algorithm, jumping gene genetic algorithm (JGGA), tabu search, and simulated annealing, have been applied for this optimization problem. Based on the simulation results, their performances are compared, and it is concluded that JGGA can outperform the other three methods in term of minimizing the synchronization and parameter estimation errors.

A hybrid genetic approach is proposed for container loading in order to meet the up-to-date customer requirements in the logistics industry. Container loading is usually modeled as a three-dimensional packing problem which is known to be NP-complete. By combining an order-based genetic algorithm with a novel heuristic placement method, the complicated and highly constrained packing problem is transformed into a simple permutation problem with a much smaller searching domain. It is guaranteed that every box is stable after placement inside the container. From the simulation results, it is demonstrated that optimal results can be duly obtained in a reasonably short period of time.

A hybrid genetic approach is proposed for the cutting operation in the clothing industry. Garment cutting is a typical strip-packing problem, which is considered to be NP-complete. With a combination of genetic algorithm (GA) and a novel heuristic algorithm, "lowest-fit-left-aligned," the cutting problem is transformed into a simple permutation problem which can be effectively solved by the GA and the searching domain is greatly reduced. From the simulation results, it is demonstrated that the optimal results can be obtained in a reasonably short period of time.