Recently, we have witnessed an infusion of calculating models based on models offered by nature, models with more or less fidelity to the original that have led to the development of various problem-solving computational procedures. Starting from the observation of natural processes at the macroscopic or microscopic level, various methods have been developed. Technological progress today allows the accelerated reproduction of natural phenomena in the laboratory, which is why a new niche has arisen in the landscape of nature-inspired methods. This niche is devoted to the emulation of artificial biological processes in computational problem-solving methods.
This paper proposes a novel approach, which is to develop novel computational methods in the field of Natural Computing based on the semi-natural process, namely Directed Evolution. In the first step we explain Directed Evolution, defined as the artificial reproduction of the process of evolution in the laboratory in order to obtain performing biological entities. For computer scientists, this provide a strong source of inspiration in the search for efficient methods of optimization. The computational model that proposed here largely overlaps with the Directed Evolution protocol, and the results obtained in the numerical experiments confirm the viability of such techniques inspired by processes which are more artificial than natural. The paper describes a novel general algorithm, inspired by Directed Evolution, which is able to solve different optimization problems, such as single optimization, multiobjective optimization and combinatorial optimization problems.