China 
Africa 
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Load balancing algorithm for computing cluster using improved cultural particle swarm optimization
Published in Jimmy C.M. Kao, Wen-Pei Sung, Civil, Architecture and Environmental Engineering, 2017
Weihua Huang, Zhong Ma, Xinfa Dai, Yi Gao, Mingdi Xu
Cultural algorithm obtains useful knowledge and information through the evolution space of micro-level, main population space, and reserves it in the evolution space of macro-level, belief space. By incorporating population evolution mechanism of genetic algorithm into self-evolution process of knowledge space, the proposed algorithm can evolve and update knowledge space cluster through selection, crossover and mutation, so as to improve global searching capacity and operational efficiency of its evolutionary operations. The framework of ICPSO algorithm is as shown in Fig. 1.
Green Roof Garden Concept for Smart Cities ‒ A Case Study
Published in Pradeep Tomar, Gurjit Kaur, Green and Smart Technologies for Smart Cities, 2019
Carlos Alberto Ochoa, Aida Yarira Reyes Escalante
Cultural algorithms have a wide variety of applications, for example in the field of engineering and in the field of robotics. Future work using cultural algorithms is related to the distribution of workgroups, social groups or social networking to support diverse problems related with smart manufacturing. Finally, cultural algorithms can be used in pattern recognition in a social database, for example fashion styling and criminal behavior, and to improve models of distribution of goods and services (Guo et al. 2017) and (Waris and Reynolds 2018).
Solar Power Estimation Methods Using ANN and CA-ANN Models for Hydrogen Production Potential in Mediterranean Region
Published in IETE Journal of Research, 2023
Robert G. Reynolds (1994) proposed the Cultural Algorithm (CA) as a subcategory of evolutionary approaches [54]. The Cultural Algorithm is a proposed optimization approach based on the observation of natural cultural evolution. To model cultural changes, this algorithm is based on certain presented hypotheses in sociology and archeology. During the evolutionary process, this method extracts domain information and produces an applicable region border to facilitate the search.