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Real-Coded Evolutionary Approaches to Unconstrained Numerical Optimization

This paper describes an application of two evolutionary algorithms to unconstrained numerical optimization. The first is a steady-state genetic algorithm that combines some wellsucceeded features of other genetic algorithms. The other is a heuristic training evolutionary approach that evolves a dynamic population to local optimal points. The algorithms are suitable to be applied to several applications in engineering and computer science. Computational tests are presented using available test functions taken from the literature.