Evolutionary design of experiments using the MapReduce framework

Authors:
James Decraene;Fanchao Zeng;Malcolm Yoke Hean Low;Wentong Cai;Yong Yong Cheng;Chwee Seng Choo
Affiliations:
Nanyang Technological University, Singapore;Nanyang Technological University, Singapore;Nanyang Technological University, Singapore;Nanyang Technological University, Singapore;Operations Research Laboratory, DSO National Laboratories, Singapore;Operations Research Laboratory, DSO National Laboratories, Singapore
Venue:
Proceedings of the 2011 Summer Computer Simulation Conference
Year:
2011

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Abstract

We examine cloud computing, using the MapReduce framework, to assist the evolutionary design of experiments method (EvoDOE). Cloud computing has recently attracted considerable attention due to the massive and scalable computational resources it can deliver. These features may potentially benefit EvoDOE, a highly computationally intensive methodology in which many computer simulations are generated and evaluated using evolutionary computation techniques. To assist this research, we implement a selection of distributed evolutionary computation techniques using the MapReduce framework. The aim of this paper is to identify the evolutionary computing model which may most efficiently exploit cloud computing for EvoDOE. Multiple series of experiments are conducted using a case study from the military domain. Specifically, red teaming experiments using an agent-based simulation of a maritime anchorage protection scenario are performed.