A Hybrid Optimization Method Using a Real - coded Multi - parent GA , Simplex & Simulated Annealing with Applications in Resolution of Overlapped Signals
نویسندگان
چکیده
This paper presents a GA algorithm for function optimization that combines the features of Simplex crossover and Simulated Aannealing. Hybridization with a local search tool like Simplex directs the GA to more promising search spaces while preventing premature local minima convergence. The use of Simulated Annealing(SA) as a mutation operator further enhances the GA's overall capability. This elitist real-coded multi-parent model is able to address the issue of slow convergence rate very well and is robust in optimizing epistatic multimodal problems. In addition to test functions, our model was applied to the resolution of overlapping signals.
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