@conference {16415,
	title = {CrossNet: a framework for crossover with network-based chromosomal representations},
	booktitle = {Proceedings of the 10th annual conference on Genetic and evolutionary computation},
	series = {GECCO {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {1057 - 1064},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {We propose a new class of crossover operators for genetic algorithms (CrossNet) which use a network-based (or graph-based) chromosomal representation. We designed CrossNet with the intent of providing a framework for creating crossover operators that take advantage of domain-specific knowledge for solving problems. Specifically, GA users supply a network which defines the epistatic relationships between genes in the genotype. CrossNet-based crossover uses this information with the goal of improving linkage. We performed two experiments that compared CrossNet-based crossover with one-point and uniform crossover. The first experiment involved the density classification problem for cellular automata (CA), and the second experiment involved fitting two randomly generated hyperplane-defined functions (hdf{\textquoteright}s). Both of these exploratory experiments support the hypothesis that CrossNet-based crossover can be useful, although performance improvements were modest. We discuss the results and remain hopeful about the successful application of CrossNet to other domains. We conjecture that future work with the CrossNet framework will provide a useful new perspective for investigating linkage and chromosomal representations.},
	keywords = {crossover, Genetic algorithms, graphs, linkage, NETWORKS, recombination},
	isbn = {978-1-60558-130-9},
	doi = {10.1145/1389095.1389290},
	url = {http://doi.acm.org/10.1145/1389095.1389290},
	author = {Stonedahl,Forrest and Rand, William and Wilensky,Uri}
}
@article {14506,
	title = {Statistical Relational Learning as an Enabling Technology for Data Acquisition and Data Fusion in Heterogeneous Sensor Networks},
	year = {2008},
	month = {2008/06/29/},
	institution = {OFFICE OF RESEARCH ADMINISTRATION AND ADVANCEMENT, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {Our work has focused on developing new cost sensitive feature acquisition and classification algorithms, mapping these algorithms onto camera networks, and creating a test bed of video data and implemented vision algorithms that we can use to implement these. First, we will describe a new algorithm that we have developed for feature acquisition in Hidden Markov Models (HMMs). This is particularly useful for inference tasks involving video from a single camera, in which the relationship between frames of video can be modeled as a Markov chain. We describe this algorithm in the context of using background subtraction results to identify portions of video that contain a moving object. Next, we will describe new algorithms that apply to general graphical models. These can be tested using existing test sets that are drawn from a range of domains in addition to sensor networks.},
	keywords = {*ALGORITHMS, *CLASSIFICATION, data acquisition, DATA FUSION, Detectors, Feature extraction, HMM(HIDDEN MARKOV MODELS), NETWORKS, NUMERICAL MATHEMATICS, PE611102, RANDOM FIELDS, STATISTICS AND PROBABILITY, TEST SETS, VIDEO SIGNALS},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA500520},
	author = {Jacobs, David W. and Getoor, Lise}
}
@article {17650,
	title = {Resilient multicast using overlays},
	journal = {IEEE/ACM Transactions on Networking},
	volume = {14},
	year = {2006},
	month = {2006/04//},
	pages = {237 - 248},
	abstract = {We introduce Probabilistic Resilient Multicast (PRM): a multicast data recovery scheme that improves data delivery ratios while maintaining low end-to-end latencies. PRM has both a proactive and a reactive components; in this paper we describe how PRM can be used to improve the performance of application-layer multicast protocols especially when there are high packet losses and host failures. Through detailed analysis in this paper, we show that this loss recovery technique has efficient scaling properties-the overheads at each overlay node asymptotically decrease to zero with increasing group sizes. As a detailed case study, we show how PRM can be applied to the NICE application-layer multicast protocol. We present detailed simulations of the PRM-enhanced NICE protocol for 10 000 node Internet-like topologies. Simulations show that PRM achieves a high delivery ratio (>97\%) with a low latency bound (600 ms) for environments with high end-to-end network losses (1\%-5\%) and high topology change rates (5 changes per second) while incurring very low overheads (<5\%).},
	keywords = {application-layer multicast protocols, Computer science, Data communication, Delay, Internet, Internet-like topologies, IP networks, loss recovery technique, Multicast, multicast data recovery scheme, Multicast protocols, Network topology, NETWORKS, overlays, Performance loss, probabilistic forwarding, probabilistic resilient multicast, Protocols, Resilience, Streaming media, telecommunication network topology, Terminology},
	isbn = {1063-6692},
	doi = {10.1109/TNET.2006.872579},
	author = {Banerjee,S. and Lee,Seungjoon and Bhattacharjee, Bobby and Srinivasan, Aravind}
}
