Ecosystem-Oriented Distributed Evolutionary Computing

TL;DR

This paper introduces a novel ecosystem-inspired distributed evolutionary computing method that combines global gene migration with local evolutionary optimization, emphasizing self-organization and scalability.

Contribution

It presents a new hybrid distributed evolutionary algorithm inspired by natural ecosystems, integrating theory from biology and computer science.

Findings

The approach demonstrates scalable optimization in distributed networks.

The hybrid model balances global gene flow with local solution refinement.

Ecosystem properties like self-organization are effectively incorporated.

Abstract

We create a novel optimisation technique inspired by natural ecosystems, where the optimisation works at two levels: a first optimisation, migration of genes which are distributed in a peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. We consider from the domain of computer science distributed evolutionary computing, with the relevant theory from the domain of theoretical biology, including the fields of evolutionary and ecological theory, the topological structure of ecosystems, and evolutionary processes within distributed environments. We then define ecosystem- oriented distributed evolutionary computing, imbibed with the properties of self-organisation, scalability and sustainability from natural ecosystems, including a novel form of distributed evolu- tionary computing. Finally, we conclude with a discussion of the apparent compromises resulting from the hybrid model created, such as the network topology.