Evolutionary Method of Population Classification According to Level of Social Resilience

Following the many natural disasters and global socio-economic upheavals of the 21st century, the concept of resilience is increasingly the subject of much research aimed at finding appropriate responses to these traumas. However, most existing work on resilience is limited to a broad cross-disciplinary panel of non-operational theoretical approaches. Thus, the study of the processes of social resilience is confronted with difficulties of modeling and a lack of appropriate analysis tools. However, the existing stratification methods are too general to take into account the specificities of the resilience and are difficult to use for non-specialists in modeling. In addition, most traditional methods of partition research have limitations including their inability to effectively exploit the research space. In this paper, we propose a classification algorithm based on the technique of genetic algorithms and adapted to the context of social resilience. Our objective function, after penalization by two criteria, allows to explore widely the space of research for solutions while favoring classes quite homogeneous and well separated between them.

@article{Tiekoura2017,
  title     = {Evolutionary Method of Population Classification According to Level of Social Resilience},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {1},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Coulibaly Kpinna Tiekoura and Brou Konan Marcellin and Babri Michel and Souleymane Oumtanaga},
  doi       = {10.14569/IJACSA.2017.080119},
  url       = {https://doi.org/10.14569/IJACSA.2017.080119}
}