Jayshree Sarma

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There has been a lot of recent interest in so-called "steady state" genetic algorithms (GAs) which, among other things, replace only a few individuals (typically 1 or 2) each generation from a fixed size population of size N. Understanding the advantages and/or disadvantages of replacing only a fraction of the population each generation (rather than the(More)
A critical part of the design of eeective EAs is to obtain a proper balance between exploration and exploitation. A key element of this balance is the selection algorithm used. Although we have for some time now understood the relative diierences between various selection methods for standard centralized EAs, our understanding of decentralized EAs has been(More)
AIM To determine the annual incidence of congenital malformations in Assam and to analyze the data. MATERIALS AND METHODS Data regarding babies born with congenital malformations in the state of Assam during the year 2006 were obtained through questionnaires and analyzed. The results were compared with similar Indian data. RESULTS The overall incidence(More)
The increasing availability of parallel computing architectures provides an opportunity to exploit this power as we scale up evolutionary algorithms (EAs) to solve more complex problems. To eeectively exploit ne grained parallel architectures, the control structure of an EA must be decentralized. This is dif-cult to achieve without also changing the(More)
Coevolutionary algorithms offer great promise as adaptive problem solvers but suffer from several known pathologies. Historically, spatially embedded coevolutionary algorithms seem to have succeeded where other coevolutionary approaches fail; however, explanations for this have been largely unexplored. We examine this idea more closely by looking at spatial(More)
Recent studies of spatially distributed EAs have formally characterized the selection pressure induced by various selection strategies applied to local neighborhoods of various sizes and shapes. These analyses provide us with the ability to predict the expected behavior of the local neighborhood EAs. In this paper we empirically validate these predictions(More)
AN ANALYSIS OF COOPERATIVE COEVOLUTIONARY ALGORITHMS R. Paul Wiegand George Mason University, 2003 Thesis Director: Dr. Kenneth A. De Jong Coevolutionary algorithms behave in very complicated, often quite counterintuitive ways. Researchers and practitioners have yet to understand why this might be the case, how to change their intuition by understanding the(More)
Traditional EAs lose diversity fairly quickly due to the strong selection pressures used to achieve good optimization performance, and thus have diiculty with non-stationary environments ((tness landscapes) unless significant algorithmic changes are made. Decentralized and spatially distributed EAs intuitively appear to be more robust in their ability to(More)