A global optimization algorithm based on the process of evolution in complex biological systems

Global optimization algorithms are ubiquitously required in scientific and engineering investigations. They are indispensable for the modeling and optimal control of complex systems such as those determining plant growth and crop yield, and are also important in the modeling of advanced AI and A-Life. To date, several models of the microscopic mechanisms involved in brain function have been proposed based on quantum dynamics. The introduction of quantum dynamics in these models may give practical hints towards the development of new algorithms and technologies. In this paper, both quantum dynamics and a model of the complex biological systems supporting evolution and consciousness are brought to bear in proposing a global optimization algorithm that can avoid locally optimal states of nonlinear functions. The algorithm is demonstrated with a simple numerical simulator written in Java. Implemented on new kinds of hardware, currently under study, such as analog computers using nonlinear electric circuits, bio-molecules and quantum micro-devices, it may be deployed as a universal algorithm to solve optimization problems.