Improving the Human Evolutionary Model: An intelligent optimization method

We are presenting improvements for the Human Evolutionary Model (HEM), this is a novelty intelligent evolutionary optimization method that should learn from experts using consensus knowledge with the aim of inferring the most suitable parameters to achieve the evolution. Since, human experts are part of the system, and it is very common that they disagree in a major or minor part of the knowledge, usually the 22 O. Montiel et al information has a considerable degree of uncertainty. HEM uses a novel concept called Mediative Fuzzy Logic (MFL) for handling doubtful and contradictory information. To demonstrate the efficiency of HEM, we are also presenting comparative results. Comparisons were made against the genetic algorithm of the Matlab’s Toolbox. In all the test that we performed the results were in favor of HEM