FPGA implementation of a multi-population PBIL algorithm

Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations.

Factors that drive Female Entrepreneurship in Armenia

Entrepreneurs play a key role in any economy. Entrepreneurship includes creativity, innovation, risk taking, planning and management and is described as transferring ideas into action. Female entrepreneurship, in particular, is considered an important tool in enabling female empowerment and emancipation. In the light of recent world events, this has become a crucial area to study and understand, especially with respect to motivations, obstacles, constraints and consequences of female entrepreneurship. Having the previous framework in attention, this thesis focuses on female entrepreneurship in a developing country - Armenia – and proposes a conceptual framework of the phenomenon. A joint cooperation between the World Bank and the European Bank for Reconstruction and Development allowed to conduct an enterprise survey in the country and from that survey a microeconomic dataset was released and applied in this research study. A logistic regression econometric method is applied to the dataset to identify and measure the relationship between female entrepreneurship and several factors such as the location, size, legal status, market and obstacles faced by Armenian firms. The study concludes that women entrepreneurs in Armenia share many common features and obstacles with their male counterparts. Moreover, gender of the top managers, the firm’s location, size, main market type, the number of competitors and full-time employees, the adoption of new marketing methods, the access to land, the tax administration system and an inadequately educated workforce are found to be statistical significant factors in the explanation of female entrepreneurship in Armenia.