Human evolutionary model: A new approach to optimization

The aim of this paper is to propose the Human Evolutionary Model (HEM) as a novel eomputational method for solv­ ing seareh and optimization problems with single or multiple objeetives, HEM is an intelligent evolutionary optimization method that uses eonsensus knowledge from experts with the aim of inferring the most suitable parameters to aehieve the evolution in an intelligent way, HEM is able to handIe experts' knowledge disagreements by the use of a novel eoneept eal1ed Mediative Fuzzy Logic (MFL). The effeetiveness of this computational method is demonstrated through several experiments that were performed using c1assieal test funetions as wel1 as eomposite test functions. We are eomparing our results against the results obtained with the Genetie Algorithm of the Matlab's Toolbox, Evolution Strategy with Covarianee Matrix Adaptation (CMA-ES), Particle Swarrn Optimizer (PSO), Cooperative PSO (CPSO), G3 model with PCX erossover (G3-PCX), Differential Evolution (DE), and Comprehensive Learning PSO (CLPSO). The results obtained using HEM outperforms the results obtained using the abovementioned optimization methods. © 2006 Elsevier lne. AH rights reserved.