Neuroscience and Psychiatry: Open Access

Evolutionary Methods Of Optimization.

 Evolutionary learning applies evolutionary algorithms in machine learning to deal with optimization problems, and has yielded encouraging outcomes in many applications. However, thanks to the heuristic nature of evolutionary optimization, most outcomes so far are empirical and lack theoretical support. Optimization Method — Evolutionary Algorithms (EAs) EAs [15–17] are stochastic optimization methods supported the evolution theory. They handle a population of candidate solutions which evolves consistent with the principles of natural selection, that's using recombination, selection and mutation processes. Evolutionary Computation has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The research, Optimization community is split on the acceptability of those techniques. One group accepts these techniques as potential heuristics for solving complex problems and therefore the other rejects them on the idea of their weak mathematical foundations. During this paper, we discuss the explanations for using EC in optimization. A quick review of Evolutionary Algorithms and their applications is provided. We also investigate the utilization of EAs for solving a two-stage transportation problem by designing a replacement algorithm. The computational results are analyzed and compared with conventional optimization techniques. Over successive generations, the population evolves toward an optimal solution.  

High Impact List of Articles

Relevant Topics in General Science