Towards an epigenetics-inspired control system for power dispatch problem

Artificial neural networks and genetic and evolutionary optimization algorithms are abstractions of observed natural processes and are found to have a widespread applicability in solving difficult problems. Epigenetics is a biological mechanism in which heritable changes are passed from one generation to another without genetic alterations. These changes allow organisms to adapt to abrupt changes in the environment. We propose and present an epigenetics-inspired learning paradigm illustrated in a power dispatch problem in which generators may fail abruptly. The proposed system is trained on optimized data, and should be able to handle a wider variety of operating conditions. The proposed idea is generic and novel but requires further application to more complex real-world control system problems to fully ascertain its value.