TY - JOUR
T1 - Human evolutionary model
T2 - A new approach to optimization
AU - Montiel, Oscar
AU - Castillo, Oscar
AU - Melin, Patricia
AU - Díaz, Antonio Rodríguez
AU - Sepúlveda, Roberto
PY - 2007/5/15
Y1 - 2007/5/15
N2 - The aim of this paper is to propose the Human Evolutionary Model (HEM) as a novel computational method for solving search and optimization problems with single or multiple objectives. HEM is an intelligent evolutionary optimization method that uses consensus knowledge from experts with the aim of inferring the most suitable parameters to achieve the evolution in an intelligent way. HEM is able to handle experts' knowledge disagreements by the use of a novel concept called Mediative Fuzzy Logic (MFL). The effectiveness of this computational method is demonstrated through several experiments that were performed using classical test functions as well as composite test functions. We are comparing our results against the results obtained with the Genetic Algorithm of the Matlab's Toolbox, Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), Particle Swarm Optimizer (PSO), Cooperative PSO (CPSO), G3 model with PCX crossover (G3-PCX), Differential Evolution (DE), and Comprehensive Learning PSO (CLPSO). The results obtained using HEM outperforms the results obtained using the abovementioned optimization methods.
AB - The aim of this paper is to propose the Human Evolutionary Model (HEM) as a novel computational method for solving search and optimization problems with single or multiple objectives. HEM is an intelligent evolutionary optimization method that uses consensus knowledge from experts with the aim of inferring the most suitable parameters to achieve the evolution in an intelligent way. HEM is able to handle experts' knowledge disagreements by the use of a novel concept called Mediative Fuzzy Logic (MFL). The effectiveness of this computational method is demonstrated through several experiments that were performed using classical test functions as well as composite test functions. We are comparing our results against the results obtained with the Genetic Algorithm of the Matlab's Toolbox, Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), Particle Swarm Optimizer (PSO), Cooperative PSO (CPSO), G3 model with PCX crossover (G3-PCX), Differential Evolution (DE), and Comprehensive Learning PSO (CLPSO). The results obtained using HEM outperforms the results obtained using the abovementioned optimization methods.
KW - Fuzzy adaptation
KW - HEM
KW - Intelligent evolutionary algorithm
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=33847651855&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2006.09.012
DO - 10.1016/j.ins.2006.09.012
M3 - Artículo
SN - 0020-0255
VL - 177
SP - 2075
EP - 2098
JO - Information Sciences
JF - Information Sciences
IS - 10
ER -