Emergence nonlinear Multifractal architecture by Hypervolume estimation algorithm for evolutionary multi-criteria optimisation

Abstract

Currently, architecture typologies and urban morphogenesis are subject to the enormous diversification of emergence mechanisms, in addition to an accelerated pace of computational approaches, seeking optimum performance competitively. Whilst environmental, material behaviour, biological design and a broad spectrum of linear form finding processes are incapable, alone, of identifying between the current complex morphology, co-evolving and the human bio-functional interrelation evolution. Only a new language grounded on Artificial Live evolution of forms, based on different methods such as Multifractal design, chaos theory, nonlinear dynamics and complexity theory could permeate and adapt to the huge scientific challenges, with continuous mutations and self-organization. The focus of this research is in the presentation of an evolutionary Multifractal architecture method, able to generate patterns that adaptively self-organize depending on the feedback, by using a Hypervolume multi-criteria optimisation estimation algorithm, based on weighted fitness function design. This convergence between the science of complexity with architecture and urban morphology forms a starting point from which the creation of both integrative and innovative architecture and urban design processes arise.