Wolfpack-inspired evolutionary algorithm and a reaction-diffusion-based controller are used for pattern formation.

The implicit social structure of population groups have been previously investigated in the literature representing enhancements in the performance of optimization algorithms. Here we introduce an evolutionary algorithm inspired by animal hunting groups (i.e. wolves). The algorithm implicitly maintains diversity in the population and performs higher than two state of the art evolutionary algorithms in the investigated case studies in this article. The case studies are to evolve a hormone-inspired system called AHHS (Artificial Homeostatic Hormone Systems) to develop spatial patterns. The complex spatial patterns are developed in the absence of any explicit spatial information. The results achieved by AHHS are presented and compared with a previous work with Artificial Neural Network (ANNs) indicating higher performance of AHHS.