Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary Algorithms in Management Applications
Evolutionary Algorithms in Management Applications
Genetic Algorithms for Pattern Recognition
Genetic Algorithms for Pattern Recognition
Learning Algorithms: Theory and Applications in Signal Processing
Learning Algorithms: Theory and Applications in Signal Processing
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
A genetic algorithm environment for star pattern recognition
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
An adaptive hybrid genetic algorithm for the three-matching problem
IEEE Transactions on Evolutionary Computation
A hybrid evolutionary approach for solving constrained optimization problems over finite domains
IEEE Transactions on Evolutionary Computation
Adaptive genetic operators based on coevolution with fuzzybehaviors
IEEE Transactions on Evolutionary Computation
A new co-mutation genetic operator
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
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In order to further improve the convergence performance of available genetic algorithms (GAs), a new operator, namely Immigration Operator (IO), was proposed in this paper. Using the IO, an improved genetic algorithm was developed. To verify the effectiveness of the IO on improving the evolutionary performances of the algorithm, two benchmarking problems had been adopted. The first one is the typical simulation problem for searching the maximum value of the advanced Goldstein & Price function in a prescribed region. The second is the well-known Traveling Salesman problem (TSP). Subsequently, the improved algorithm was applied to search the effective criteria for monitoring the working condition of engine valves. The object inspected in the experiments was the sixth exhaust valve of a 6135-typed diesel engine. Both the simulated and practical experiments suggest that, after adopting the IO, a higher rate of convergence is achieved by the improved algorithm. Particularly in solving the kind of TSP problems, the crossover operator is handicapped in avoiding the morbid solution (i.e. the same city is traveled for multiple times in a same tour). In contrast, the IO provides an additional motivity for driving the evolution.