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Andrea G. B. Tettamanzi

Bio: Andrea G. B. Tettamanzi is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Evolutionary algorithm & Fuzzy logic. The author has an hindex of 28, co-authored 227 publications receiving 2651 citations. Previous affiliations of Andrea G. B. Tettamanzi include University of California, Berkeley & University of Luxembourg.


Papers
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Book
07 Sep 2001
TL;DR: This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing and is developed from courses given by the authors and offers numerous illustrations as well.
Abstract: Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically.This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

163 citations

Journal ArticleDOI
TL;DR: Theoretical results are in agreement with experimental values, showing that the selection intensity can be controlled by using different update methods, and it is seen that the usual logistic approximation breaks down for low-dimensional lattices and should be replaced by a polynomial approximation.
Abstract: In this paper, we present quantitative models for the selection pressure of cellular evolutionary algorithms on regular one- and two-dimensional (2-D) lattices. We derive models based on probabilistic difference equations for synchronous and several asynchronous cell update policies. The models are validated using two customary selection methods: binary tournament and linear ranking. Theoretical results are in agreement with experimental values, showing that the selection intensity can be controlled by using different update methods. It is also seen that the usual logistic approximation breaks down for low-dimensional lattices and should be replaced by a polynomial approximation. The dependence of the models on the neighborhood radius is studied for both topologies. We also derive results for 2-D lattices with variable grid axes ratio.

108 citations

Journal ArticleDOI
TL;DR: In this article, an integrated framework for modeling and evaluating the economic impacts of environmental dispatching and fuel switching is presented, which explores the potential for operational changes in electric utility commitment and dispatching to achieve least-cost operation while complying to rigorous environmental standards.
Abstract: An integrated framework for modeling and evaluating the economic impacts of environmental dispatching and fuel switching is presented in this paper It explores the potential for operational changes in electric utility commitment and dispatching to achieve least-cost operation while complying to rigorous environmental standards The work reported here employs a heuristics-guided evolutionary algorithm to solve this multiobjective constrained optimization problem, and provides the decision maker a whole range of alternatives along the Pareto-optimal frontier Heuristics are used to ensure the feasibility of each solution, and to reduce the computation time The capabilities of this approach are illustrated via tests on a 19-unit system Various emission compliance strategies are considered to reveal the economic trade-offs that come into play

92 citations

Proceedings ArticleDOI
25 Jun 2005
TL;DR: It is shown that, to good approximation, randomly structured and panmictic populations have the some growth behavior and that global selection intensity depends on the update policy.
Abstract: We present discrete stochastic mathematical models for the growth curves of synchronous and synchronous evolutionary algorithms with populations structured ccording to a random graph. We show that, to good approximation, randomly structured and panmictic populations have the some growth behavior. Furthermore, we show that global selection intensity depends on the update policy. The validity of the models is confirmed by comparison with experimental results of simulations. We also present experimental results on small-world nd scale-free population graph topologies. We show that they lead to qualitatively similar results. However, the different nature of the nodes can be exploited to obtain more varied evolutionary behavior.

78 citations


Cited by
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01 Jan 2002

9,314 citations

Book ChapterDOI
31 Jan 1963

2,885 citations

01 Jan 2004
TL;DR: A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented and it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. ________________________________________ 1)International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: 510-643-7684. E-mail: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn-Ring 6, D-81739 Muenchen, Germany. Fax: 01149-636-44577, Email:rainer.storn@zfe.siemens.de. 2)836 Owl Circle, Vacaville, CA 95687, kprice@solano.community.net.

2,168 citations

Book
26 Mar 2008
TL;DR: A unique overview of this exciting technique is written by three of the most active scientists in GP, which starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination until high-fitness solutions emerge.
Abstract: Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.

1,856 citations