A new evolutionary algorithm using shadow price guided operators

The genetic algorithm (GA) is a popular global search algorithm. It has been used successfully in many fields, however, it is still challenging for the GA to obtain optimal solutions for complex problems. Another problem is that the GA can take a very long time to solve difficult problems. This paper proposes a new evolutionary algorithm that uses the fitness value to measure overall solutions and shadow prices to evaluate components. New shadow price guided operators are used to achieve good measurable evolutions. The new algorithm is used first to solve a simple optimization function and then applied to the complex traveling salesman problem (TSP). Simulation results have shown that the new shadow price guided evolutionary algorithm is effective in terms of performance and efficient in terms of speed.