Evolution inspired development of Multi-level Selection Genetic Algorithm for general applications

Genetic algorithms are used in a range of diverse applications including: spacecraft antenna design, AI in computer games, determining which genes cause illness and portfolio selection for financial investments. There is a vibrant community of researcher and industrialists developing novel methods to improve the performance of these algorithms allowing more complex problems to solve and allowing their use in new applications.
Once such method was developed in the University of Southampton (10.1088/1748-3190/aad2e8 and 10.1016/j.swevo.2018.09.005) which shows leading performance as a general solver, meaning it performs well across a wide range of problems, and shows particularly good preservation of the diversity of a population. It implements the Multi-Level Selection Theory and co-evolutionary approaches to improve the performance of the algorithm and already shows leading performance on a number of benchmarking problems and real world applications. However, the method is still new and there are a number of improvements that can be made and so this project will focus on its continued development.
The inspiration for this algorithm is already heavily based in evolutionary theory based on the idea that mechanisms that accelerate the rate of evolution in the natural world, will similarly accelerate the speed of the genetic algorithm. In this project we will take inspiration from epigenetics, transgenerational plasticity, social evolution and multi-level selection theory to test if the performance of the genetic algorithm can be similarly enhanced. Fusing contemporary evolutionary theory with an already highly efficient genetic algorithm has the potential to revolutionise evolutionary computation.