# A Doubly Distributed Genetic Algorithm for Network Coding

## TL;DR

This paper introduces a novel doubly distributed genetic algorithm for network coding that distributes genotypes across network nodes and pipelines candidate solutions temporally, significantly improving convergence time.

## Contribution

The paper proposes a new genetic algorithm that distributes genotypes across network nodes and pipelines solutions temporally, enhancing efficiency in network coding applications.

## Key findings

- Significant reduction in running time due to genotype distribution.
- Improved convergence speed with minimal penalties from temporal pipelining.
- Effective use of distributed computation in network coding contexts.

## Abstract

We present a genetic algorithm which is distributed in two novel ways: along genotype and temporal axes. Our algorithm first distributes, for every member of the population, a subset of the genotype to each network node, rather than a subset of the population to each. This genotype distribution is shown to offer a significant gain in running time. Then, for efficient use of the computational resources in the network, our algorithm divides the candidate solutions into pipelined sets and thus the distribution is in the temporal domain, rather that in the spatial domain. This temporal distribution may lead to temporal inconsistency in selection and replacement, however our experiments yield better efficiency in terms of the time to convergence without incurring significant penalties.