The Seamless Peer and Cloud Evolution Framework

Abstract

Evolutionary algorithms are increasingly being applied to problems that are too computationally expensive to run on a single personal computer due to costly fitness function evaluations and/or large numbers of fitness evaluations. Here, we introduce the Seamless Peer And Cloud Evolution (SPACE) framework, which leverages bleeding edge web technologies to allow the computational resources necessary for running large scale evolutionary experiments to be made available to amateur and professional researchers alike, in a scalable and cost-effective manner, directly from their web browsers. The SPACE framework accomplishes this by distributing fitness evaluations across a heterogeneous pool of cloud compute nodes and peer computers. As a proof of concept, this framework has been attached to the RoboGen TM open-source platform for the co-evolution of robot bodies and brains, but importantly the framework has been built in a modular fashion such that it can be easily coupled with other evolutionary computation systems.

Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories

“…Leclerc et al [9] proposed a cloud-based framework facilitating large scale evolutionary experiments. Their framework provided a master-slave architecture with nodes communicating via JSON over HTTP.…”

“…Hence, if the current is characterised with a higher CI than the global Pareto Front obtained 6 A solution in a Pareto Front approximation is non-dominated if it includes the best value among all the solutions in that front for at least one objective. if of obtained after stage s is higher than that of stage (s-1) then continue; else 8 Delete islands that meet island deletion criteria; 9 Create one island with randomly generated individuals; end end 10 return ;…”

“…7 A Pareto Front approximation strictly dominates another if the former contains at least one solution that has a better value than any solutions in the latter for all objectives. if of obtained after stage s is higher than that of stage (s-1) then 8 Delete all islands that do not provide new solutions to ; else 9 Create one island with randomly generated individuals; end end 10 return ;…”

Cloud-Based Integrated Process Planning and Scheduling Optimisation via Asynchronous Islands

“…The original GAs were executed sequentially and hence they were notorious for low speed [7]. Several techniques have been proposed to alleviate this problem, including parallel execution of GAs [5,8,9].…”

Alife in the Clouds: A Short Review of Applications of Artificial Life to Cloud Computing and Back

“…Cloud computing, for instance, offers the possibility of instantiating and using computing nodes at a cost that is a fraction of buying them, or even at no cost if you land an academic grant or your problem falls within the boundaries of the free tier cloud hosts offer. Using these nodes in the same way, we used computers in a data center or under our desktop, it is straightforward to port a parallel implementation of whatever algorithm to a cloud-based one; this presents some challenges and has in fact been addressed in several papers (Radenski et al, 2013;Leclerc et al, 2016); for instance (Medel et al, 2017) studies how to create models of complex systems using cloud resources (Gutierrez-Garcia and Sim 2010; Merelo-Guervós et al, 2011;Meri et al, 2013) presents a system that uses free cloud storage services as a device for interchanging individuals in an evolutionary algorithm; Kopp (2016) reviews technologies that are, in general, available for simulation, focusing on defense applications; however, simulation of adaptive behavior and agent-based systems form the basis of artificial life and it could be applied to it. Several other artificial life systems that use the cloud for storage are presented in a recent review (Taylor et al, 2016).…”