Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing

TL;DR

This paper uses evolutionary algorithms to analyze the balance between pleiotropy and redundancy in networks, focusing on how factors like failure rates and repair influence optimal design choices.

Contribution

It introduces a novel application of genetic algorithms to study trade-offs between pleiotropy and redundancy in network robustness and cost.

Findings

Higher link failure probability favors redundancy.

Increased repair rates reduce the need for redundancy.

Optimal balance depends on network size and failure characteristics.

Abstract

Evolutionary computation algorithms are increasingly being used to solve optimization problems as they have many advantages over traditional optimization algorithms. In this paper we use evolutionary computation to study the trade-off between pleiotropy and redundancy in a client-server based network. Pleiotropy is a term used to describe components that perform multiple tasks, while redundancy refers to multiple components performing one same task. Pleiotropy reduces cost but lacks robustness, while redundancy increases network reliability but is more costly, as together, pleiotropy and redundancy build flexibility and robustness into systems. Therefore it is desirable to have a network that contains a balance between pleiotropy and redundancy. We explore how factors such as link failure probability, repair rates, and the size of the network influence the design choices that we explore using genetic algorithms.