Generating artificial populations using a multi-level fuzzy inference engine

The design of complex artificial populations is the first step in simulating evolution during the time span of socioeconomic variables as the family income. In this paper, a new hybrid model based on Monte-Carlo simulation and fuzzy inference is described to design environmental conditions, the basic socio-economic structure and to determine the causes for mortality in an artificial population. The model is based on three main databases that describe the characteristics of the environment, individuals and mechanisms (mortality). These expert-based characteristics guide the simulation model which has a fuzzy inference engine to evaluate fuzzy dependence relationships. These relationships have been formulated to automatically determine complex environmental and individual characteristics as well as mechanism parameters, and they are based on expert knowledge. An artificial population has been designed with satisfactory results when critical design factors are carefully adjusted.

• Type: Conference
• Year: 2008
• Where: WSC
• Authors: Carlos R. Garcia-Alonso, Gabriel M. Perez-Alcala
• Keywords: Artificial Population, Complex Artificial Populations, Fuzzy Inference, Modeling And Simulation, WSC 2008