Bioinspired Modularity in Evolutionary Computation and a Rule-Based Logic Design Solutions for Shared Office Space

Abstract

Evolutionary computation is a population-based problem solver that is characterized by a stochastic optimization in order to solve both a single objective and multiple objectives. Previous evolutionary computational researches provided various design options and improved optimization through being evolved with fitness criteria, especially when multiple design objectives conflict with one another. In this paper, a rule-based algorithm was combined with the evolutionary computational process to propose an assembly logic of the modules and to improve an architectural building design in order to adapt to environmental changes. Two algorithms-a rule based and generative algorithm-proceeded simultaneously and provided various options as well as optimization in real time. For the experiment set-up, existing buildings were divided into each module; the modules were reinterpreted and reassembled with the logic driven by Evolutionary Developmental Biology. The conclusion is that when a rule based logic is combined with a developmental algorithm with a modular system, it is more efficient for the design process to be analyzed, evaluated, and optimized. The ultimate outcome provides various options in a short amount of time.