Explanation and evaluation of Charles Darwin's biological evolutionary processes in exploratory architectural design

Biological evolution through genetic diversity causes morphological diversity, which leads to a high level of adaptation, performance and resource efficiency. However, the biological design resulting from the evolution in the non-linear method is often non-intuitive and unexpected. Undirected evolutionary processes are very good at discovering new design possibilities in an endless way. Therefore, biological evolution is completely different in the gradual and permanent line of convergence of technical optimization with defined and fixed proportionality functions. Evolutionary algorithms based on Darwin's principles are mainly developed to solve multi-criteria problems in technology. Technical goals are defined as fitness functions and the evolutionary mechanisms of selection, inheritance, reproduction and mutation are used as stochastic optimization processes. These metacognitive algorithms do not include recent insights into the micro and macro evolutionary mechanisms resulting from genomics, phylogenomics and population genomics. Like natural evolution, the architectural design process is an endless process that discovers possible solutions. However, in order to guide this wide and dynamic design space, most of the design methods in architecture are based on the typology method. Designers, based on their knowledge and understanding of the problems, usually limit the solution space to a specific, interpretive, spatial or programmatic structure that is iteratively matched to specific design requirements. The inherent limitations of typology-based methodologies exclude a wide range of effective types and better design types. On the other hand, the dynamic of biological evolution shows ways to increase the design space that continuously leads to new and unknown possibilities that are potentially in a new set of typologies that still push the limits. They fulfill Therefore, in architecture, evolutionary processes correspond more to discovery processes compared to optimization tools.