Dynamic System Modeling of Evolutionary Algorithms

Evolutionary algorithms are population-based, metaheuristic, black-box optimization techniques from the wider family of evolutionary computation. Optimization algorithms within this family are often based on similar principles and routines inspired by biological evolution. Due to their robustness, the scope of their application is broad and varies from physical engineering to software design problems. Despite sharing similar principles based in common biological inspiration, these algorithms themselves are typically viewed as black-box program routines by the end user, without a deeper insight into the underlying optimization process. We believe that shedding some light into the underlying routines of evolutionary computation algorithms can make them more accessible to wider engineering public. In this paper, we formulate the evolutionary optimization process as a dynamic system simulation, and provide means to prototype evolutionary optimization routines in a visually comprehensible framework. The framework enables engineers to follow the same dynamic system modeling paradigm, they typically use for representation of their optimization problems, to also create the desired evolutionary optimizers themselves. Instantiation of the framework in a Matlab-Simulink library practically results in graphical programming of evolutionary optimizers based on data-flow principles used for dynamic system modeling within the Simulink environment. We illustrate the efficiency of visual representation in clarifying the underlying concepts on executable flow-charts of respective evolutionary optimizers and demonstrate features and potential of the framework on selected engineering benchmark applications.