The aim of this research was to investigate the influence of temperature (25-85 ºC), water:seed ratio (30-70), pH (5-7), and salt concentration (0-0.6 M NaCl) with respect to yield, apparent viscosity, specific optical rotation, and absorbance of extracted mucilage from Lallemantia royleana seeds through response surface regression modeling and to search for optimal extraction conditions by applying Simple- and Multi-Genetic Algorithms (GAs) on the models. The analysis of response surface models revealed that temperature and water:seed ratio were found to have a significant influence on yield, apparent viscosity, specific optical rotation, and absorbance of the extracted mucilage while pH and salt concentration had only minor effects. The optimization approaches used in this research were demonstrated to be efficient. Both GAs were able to determine the optimal conditions for the mucilage extraction process but the Multi-GA was more efficient than the Simple-GA. The optimal extraction conditions were 85 ºC, 59:1, 7.5, and 0 M for temperature, water:seed ratio, pH, and salt concentration, respectively. The optimal conditions were confirmed by verification techniques using additional experimental data. The content of carbohydrates, proteins, lipids, ash and moisture were determined both in the seeds and the extracted mucilage under optimum conditions by standard methods. Proximate analysis of the optimized mucilage revealed carbohydrate as the major component (61.74 %) with protein (0.87 %) and ash (8.33 %) as minor constituents.
