@article {79,
	title = {Biological model for information retrieval},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {123 - 129},
	abstract = {Heuristic methods based on biological aspects have been successfully used in many computer science areas of research, including information retrieval (IR). This let us ask: why there is no biological environment for the problem of information retrieval. In this paper, we tried to introduce a new biological environment and model for information retrieval that broad the modeling concept from the mathematical formula that simulates the basic elements of IR problem to their dual schemas in biology. It{\textquoteright}s a new way of thinking of, and dealing with, the problem of information retrieval.},
	keywords = {Biological environment, Biological model, Genetic algorithms, Information retrieval},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350691855\&partnerID=40\&md5=22f8442d6b340f89f18dd39e3c99ed99},
	author = {Girgis, M.R. and Aly, A.E.-M.A. and Latef, B.A.A. and El-Gamil, B.R.}
}
@article {110,
	title = {Multi-objective mapping for NoC architectures},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2007},
	pages = {378 - 384},
	abstract = {In order to improve the performance of current embedded systems, Network -on-Chip (NoC) offers many advantages, especially in terms of flexibility and low cost. Applications require more and more intensive computations, especially multimedia applications such as video encoding. Developing product and application using such an architecture offers many challenges and opportunities. Many tools will be required to develop a NoC architecture for a specific application. A toot which can map and schedule an application or a set of applications to a given NoC architecture will be essential and must be able to satisfy many relative trade-offs (real-time, performance, low power consumption, time to market, re-usability, cost, area, etc). In this paper, we survey most approaches used to solve this problem with different goals. We then describe our approach based on a genetic algorithm and its implementation and its design objective.},
	keywords = {Genetic algorithms, Mapping objective, MPSoC, Network-on-chip, Optimization},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-49149093560\&partnerID=40\&md5=0f2dbcdf8982dc893dbcaccefdebbc45},
	author = {Benyamina, A. El H. and Boulet, P.}
}
@article {65,
	title = {Optimization of Boolean queries in information retrieval systems using genetic algorithms - Genetic programming and fuzzy logic},
	journal = {Journal of Digital Information Management},
	volume = {4},
	year = {2006},
	note = {Export Date: 10 July 2014},
	month = {2006},
	pages = {249 - 254},
	abstract = {This paper proposes to use two information retrieval system models (Boolean information retrieval model and extended Boolean (fuzzy) information retrieval model). These models differ by using Boolean queries or fuzzy weighted queries. It also proposes a way for optimizing user query for the two models by using genetic programming and fuzzy logic. And proposes to use more number of Boolean operators (AND, OR, XOR, OF, and NOT) instead of the standard Boolean operators (AND, OR, and NOT), and use weights for Boolean operators and for terms in fuzzy models.},
	keywords = {Boolean operator weights, Boolean query, Fuzzy logic, Genetic algorithms, Genetic programming, Information retrieval, Term weights},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33845945357\&partnerID=40\&md5=bb2fe8678839544324f8ce90e9dca876},
	author = {Owais, S.S.J.}
}
