Citation:
Ashutosh Tiwari, Gokop Goteng and Rajkumar Roy, Evolutionary Computing within Grid Environment. Studies in Computational Intelligence, 66, pg. 229-248
Abstract:
Evolutionary computing (EC) techniques such as genetic algorithm (GA), genetic
programming (GP), evolutionary programming (EP) and evolution strategies (ES)
mimic nature through natural selection to perform complex optimisation processes
that require more than one solutions. Grid-enabled environment provides suitable
framework for EC techniques due to its computational and data capabilities. In
addition, the semantic and knowledge Grids aid in the design search and
exploration for multi-objective optimisation tasks. This chapter explores some
problem solving environments such as Geodise (Grid-Enabled Optimisation Design
Search for Engineering), FIPER (Federated Intelligent Product Environment),
SOCER (Service-Oriented Concurrent Environment), DAME (Distributed Aircraft
Maintenance Environment) and Globus toolkit to demonstrate how EC techniques can
be performed more efficiently within Grid environment. Service-oriented and
autonomic computing features of Grid are discussed to highlight how EC
algorithms can be published as services by service providers and used by service
requestors dynamically. Grid computational steering and visualisation are
features that can be used for real-time tuning of parameters and visual display
of optimal solutions. This chapter demonstrates that grid-enabled evolutionary
computing marks the future of optimisation techniques.