Cluster energy optimizing genetic algorithm

Abstract

Nanoclusters are small clumps of atoms of one or several materials. A cluster possesses a unique set of material properties depending on its configuration (i.e. the number of atoms, their types, and their exact relative positioning). Finding and subsequently testing these configurations is of great interest to physicists in search of new advantageous material properties. To facilitate the discovery of ideal cluster configurations, we propose the Cluster Energy Optimizing GA (CEO-GA), which combines the strengths of Johnston's BCGA [18], Pereira's H-C&S crossover [25], and two new mutation operators: Local Spherical and Center of Mass Spherical. The advantage of CEO-GA is its ability to evolve optimally stable clusters (those with lowest potential energy) without relying on local optimization methods, as do other commonly used cluster evolving GAs, such as BCGA.

Adaptable and stable decentralized task allocation for hierarchical domains

“…GAs are a type of optimization algorithms inspired by the mechanisms of natural selection, such as survival of the fittest, genetic mutations, and inheritance 0.05 1.00 0.50 0.20 0.10 0.06 0.04 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 through gene recombination. GAs have been successfully applied toward a variety of complex optimization problems, such as evolving atom positions within metallic nano-cluster formations (Kazakova et al, 2013), flying drone path planning (Ragusa et al, 2017), and even the evolution of neural network topologies (Stanley & Miikkulainen, 2002). GAs are a subset of evolutionary computation approaches.…”