Institution
Center for Discrete Mathematics and Theoretical Computer Science
Facility•Piscataway, New Jersey, United States•
About: Center for Discrete Mathematics and Theoretical Computer Science is a facility organization based out in Piscataway, New Jersey, United States. It is known for research contribution in the topics: Local search (optimization) & Optimization problem. The organization has 140 authors who have published 175 publications receiving 2345 citations.
Topics: Local search (optimization), Optimization problem, Very-large-scale integration, Auxiliary function, Nonlinear programming
Papers published on a yearly basis
Papers
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TL;DR: The common appearance of the structural motif in a functionally important part of the receptors suggests hypotheses for kinase regulation and signal transduction.
Abstract: The recent rapid growth of protein sequence databases is outpacing the capacity of researchers to biochemically and structurally characterize new proteins. Accordingly, new methods for recognition of motifs and homologies in protein primary sequences may be useful in determining how these proteins might function. We have applied such a method, an iterative learning algorithm, to analyze possible coiled coil domains in histidine kinase receptors. The potential coiled coils have not yet been structurally characterized in any histidine kinase, and they appear outside previously noted kinase homology regions. The learning algorithm uses a combination of established sequence patterns in known coiled coil proteins and histidine kinase sequence data to learn to recognize efficiently this coiled coil-like motif in the histidine kinases. The common appearance of the structural motif in a functionally important part of the receptors suggests hypotheses for kinase regulation and signal transduction.
77 citations
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TL;DR: The results show that many selection methods are inappropriate for finding polymorphic Nash solutions to variable-sum games.
Abstract: We use evolutionary game theory (EGT) to investigate the dynamics and equilibria of selection methods in coevolutionary algorithms. The canonical selection method used in EGT is equivalent to the standard "fitness-proportional" selection method used in evolutionary algorithms. All attractors of the EGT dynamic are Nash equilibria; we focus on simple symmetric variable-sum games that have polymorphic Nash-equilibrium attractors. Against the dynamics of proportional selection, we contrast the behaviors of truncation selection, (/spl mu/,/spl lambda/),(/spl mu/+/spl lambda/), linear ranking, Boltzmann, and tournament selection. Except for Boltzmann selection, each of the methods we test unconditionally fail to achieve polymorphic Nash equilibrium. Instead, we find point attractors that lack game-theoretic justification, cyclic dynamics, or chaos. Boltzmann selection converges onto polymorphic Nash equilibrium only when selection pressure is sufficiently low; otherwise, we obtain attracting limit-cycles or chaos. Coevolutionary algorithms are often used to search for solutions (e.g., Nash equilibria) of games of strategy; our results show that many selection methods are inappropriate for finding polymorphic Nash solutions to variable-sum games. Another application of coevolution is to model other systems; our results emphasize the degree to which the model's behavior is sensitive to implementation details regarding selection-details that we might not otherwise believe to be critical.
75 citations
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TL;DR: Comparison results show that the proposed estimation approach is much more efficient than the classic method, and the GExpAR models are more powerful in modeling the nonlinear time series.
71 citations
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TL;DR: It is shown that the sensor-constrained version of the problem is polynomially equivalent to the asymmetric k-center problem and that the time- Constrained versions of the problems are polyno-magnificent to the dominating set problem.
71 citations
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TL;DR: This work has demonstrated both empirically and theoretically that the high value of the penalty parameter is detrimental to convergence, specially for functions with multiple local minimizers, hence, the penalty function can dispense with the penalty parameters.
Abstract: We propose a differential evolution-based algorithm for constrained global optimization. Although differential evolution has been used as the underlying global solver, central to our approach is the penalty function that we introduce. The adaptive nature of the penalty function makes the results of the algorithm mostly insensitive to low values of the penalty parameter. We have also demonstrated both empirically and theoretically that the high value of the penalty parameter is detrimental to convergence, specially for functions with multiple local minimizers. Hence, the penalty function can dispense with the penalty parameter. We have extensively tested our penalty function-based DE algorithm on a set of 24 benchmark test problems. Results obtained are compared with those of some recent algorithms.
61 citations
Authors
Showing all 148 results
Name | H-index | Papers | Citations |
---|---|---|---|
Aravind Srinivasan | 60 | 266 | 13711 |
Ding-Zhu Du | 52 | 421 | 13489 |
Elena N. Naumova | 47 | 232 | 8593 |
Rebecca N. Wright | 37 | 113 | 4722 |
Boris Mirkin | 35 | 178 | 6722 |
Mona Singh | 32 | 91 | 5451 |
Fred S. Roberts | 32 | 181 | 5286 |
Tanya Y. Berger-Wolf | 31 | 135 | 3624 |
Rephael Wenger | 26 | 67 | 1900 |
Marios Mavronicolas | 26 | 151 | 2880 |
Seoung Bum Kim | 26 | 165 | 2260 |
M. Montaz Ali | 26 | 101 | 3093 |
Lazaros K. Gallos | 24 | 69 | 4770 |
Myong K. Jeong | 24 | 95 | 1955 |
Nina H. Fefferman | 23 | 107 | 2362 |