HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation

Today, there is a substantial need for population evolution modeling in multidisciplinary research areas, such as social sciences (sociology, anthropology etc.), which can neither be solved formally, nor empirically at global scale, thus requiring the development of heuristic techniques, like evolutionary algorithms. Therefore, existent methodologies of social simulation can be extended from microenvironments to large scale modeling of extremely complex systems, as it is the case of human evolution. The modeling of population evolution prediction is currently used in high interest areas, from migration flows, to financial crisis simulation, to the free-market economy models, as well as multi-national family dynamics and cultural aspects. The high performance of evolutionary computing has been already proven in the context of virus population evolution, be they biological or cybernetic, thus making them the perfect method for our endeavour. This paper presents the design and implementation of a soft computing application, namely HELGA—heterogeneous encoding lifelike genetic algorithm. HELGA was designed for the modeling and simulation of Earth population evolution, on a global scale, throughout multiple historical eras. The model shows the tendency of human evolution towards one mixed race. The algorithm takes into account the influence of social factors, such as the Industrial Revolution or the discovery of the New World. Our method, for which all constraints have been based on validated research, has aligned precisely to real historical events: it anticipated the end of antiquity, the industrial era, the baby boom phenomenon and so on. For example, the poor health conditions of the Middle Ages have caused a drastic drop in population size. The presented model shows the valiant result of one final race, and although this theory cannot be formally proved in the present, we consider that our results can be used as hypothesis for future social science research. Also, HELGA can be extended with new capabilities and constraints.