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Job shop scheduling problem is a typical NP-hard problem. In this paper, new designed crossover and mutation operators based on the characteristic of the job shop problem itself are specifically designed. Based on these, an improved genetic algorithm is proposed. The computer simulations are made on a set of benchmark problems and the results indicate the effectiveness of the proposed algorithm.
For nonlinear bi-level programming problems in which the follower's problem is linear, the paper develops a genetic algorithm based on a mixed encoding technique. At first, each individual consists of two parts, the first part is the leader's variable values using real-encoding, whereas the second one is the sequence number of basic variables of the follower's programming, which are some integers...
In this paper, the level-set evolution is exploited in the design of a novel evolutionary algorithm (EA) for global optimization. An application of Latin squares leads to a new and effective crossover operator. This crossover operator can generate a set of uniformly scattered offspring around their parents, has the ability to search locally, and can explore the search space efficiently. To compute...
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