Optimizing genetic algorithm strategies for evolving networks

TL;DR

This paper investigates how genetic algorithms can optimize network designs with fluctuating demands, focusing on operator effects and the trade-off between redundancy and pleiotropy for cost and reliability.

Contribution

It introduces a systematic analysis of genetic algorithm operators and their impact on evolving networks with redundancy and pleiotropy considerations.

Findings

Genetic operators significantly influence network quality.

Redundancy improves reliability at higher costs.

Pleiotropy offers a trade-off between cost and robustness.

Abstract

This paper explores the use of genetic algorithms for the design of networks, where the demands on the network fluctuate in time. For varying network constraints, we find the best network using the standard genetic algorithm operators such as inversion, mutation and crossover. We also examine how the choice of genetic algorithm operators affects the quality of the best network found. Such networks typically contain redundancy in servers, where several servers perform the same task and pleiotropy, where servers perform multiple tasks. We explore this trade-off between pleiotropy versus redundancy on the cost versus reliability as a measure of the quality of the network.