Distributed Computing for Genetic Approximation Algorithms for the Kissing Problem (n-dimensions)

Abstract

Utilizing distributed computing for genetic optimization algorithms with n-dimensions and kissing numbers in their approximation, helps offload data and increase efficiency in approximation. In order to demonstrate this, we shall utilize mathematical proofs centered around N-dimensional vectors and arrays, as well as exponential dimensional analysis. Utilizing these proofs in optimization algorithms can have processed data offloaded through a shared network of computers running simultaneous multi-threaded computational processes. One can build a computational model based off of mathematical constraints viewed as higher dimensional complexity. Formulating such proofs is based off of degree of certainty versus uncertainty in the approximation, and which processing task should be optimized in order to yield the best result.