This paper presents a new approach for cardiac beat interpretation, based on a direct integration between a model and observed ECG signals. Physiological knowledge is represented by means of a semi-quantitative model ...
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This paper presents a new approach for cardiac beat interpretation, based on a direct integration between a model and observed ECG signals. Physiological knowledge is represented by means of a semi-quantitative model of the cardiac electrical activity. The interpretation of cardiac beats is formalized as an optimization problem, by minimizing an error function defined between the model's output and the observations. evolutionary algorithms (EAs) are used as the search technique in order to obtain the set of model parameters reproducing at best the observed phenomena. Examples of model adaptation to three different kinds of cardiac beats are presented. Preliminary results show the potentiality of this approach to reproduce and explain complex pathological disorders and to better localize their origin., (C) 2002 Elsevier Science B.V. All rights reserved.
A method of assessing the optimum pumping rates of coastal aquifers based on nonlinear optimization and evolutionary algorithms (EA) is developed. The objective is to maximize the total pumping rate while protecting t...
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A method of assessing the optimum pumping rates of coastal aquifers based on nonlinear optimization and evolutionary algorithms (EA) is developed. The objective is to maximize the total pumping rate while protecting the wells from sea water intrusion. The formulation of the constraints is based on numerical simulation of the freshwater flow equations. The simulation model is based on the sharp interface and the Ghyben-Herzberg approximation and is applicable to unconfined aquifers and steady-state flow. The single potential formulation of [Water Resour. Res. 12 (1976) 1165] is followed and the governing equations are solved numerically using finite differences. The numerical model can handle aquifers of complex shapes, nonuniform hydraulic conductivity, nonuniform distribution of surface recharge, etc. The constraints are nonlinear with respect to the decision variables resulting in a nonlinear optimization problem. Two optimization methods are investigated, specifically Sequential Quadratic Programming (SQP) and evolutionary algorithms (EA). SQP requires less computer time than EA but can get stuck on local optimum solutions. The simulation and optimization methodology is applied to a real unconfined coastal aquifer in the Greek island of Kalymnos for determining the optimal pumping rates while protecting the wells from sea water intrusion. (C) 2004 Elsevier B.V. All rights reserved.
This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: first, the different families of EAs have naturally converg...
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This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: first, the different families of EAs have naturally converged in the last decade while parallel EAs (PEAS) seem still to lack unified studies, and second, there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA.
A framework for hybridizing evolutionary algorithms with the branch-and-bound algorithm (B&B) is presented in this paper. This framework is based on using B&B as an operator embedded in the evolutionary algori...
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A framework for hybridizing evolutionary algorithms with the branch-and-bound algorithm (B&B) is presented in this paper. This framework is based on using B&B as an operator embedded in the evolutionary algorithm. The resulting hybrid operator will intelligently explore the dynastic potential (possible children) of the solutions being recombined, providing the best combination of formae (generalized schemata) that can be constructed without introducing implicit mutation. As a basis for studying this operator, the general functioning of transmitting recombination is considered. Two important concepts are introduced, compatibility sets, and granularity of the representation. These concepts are studied in the context of different kinds of representation: orthogonal, non-orthogonal separable, and non-separable. The results of an extensive experimental evaluation are reported. It is shown that this model can be useful when problem knowledge is available in the form of an optimistic evaluation function. Scalability issues are also considered. A control mechanism is proposed to alleviate the increasing computational cost of the algorithm for highly multidimensional problems.
Chemical reactors are employed to produce several materials, which are utilized in numerous applications. The wide use of these chemical engineering units shows their importance as their performance vastly affects the...
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Chemical reactors are employed to produce several materials, which are utilized in numerous applications. The wide use of these chemical engineering units shows their importance as their performance vastly affects the production process. Thus, improving these units will develop the process and/or the manufactured material. Multi-objective optimization (MOO) with evolutionary algorithms (EA's) has been used to solve several real world complex problems for improving the performance of chemical reactors with conflicting objectives. These objectives are of different nature as they could be economy, environment, safety, energy, exergy and/or process related. In this review, a brief description for MOO and EA's and their several types and applications is given. Then, MOO studies, which are related to the materials' production via chemical reactors, those were conducted with EA's are classified into different classes and discussed. The studies were classified according to the produced material to hydrogen and synthesis gas, petrochemicals and hydrocarbons, biochemical, polymerization and other general processes. Finally, some guidelines are given to help in deciding on future research.
Bi-level optimisation problems have gained increasing interest in the field of combinatorial optimisation in recent years. In this paper, we analyse the runtime of some evolutionary algorithms for bi-level optimisatio...
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Bi-level optimisation problems have gained increasing interest in the field of combinatorial optimisation in recent years. In this paper, we analyse the runtime of some evolutionary algorithms for bi-level optimisation problems. We examine two NP-hard problems, the generalised minimum spanning tree problem and the generalised travelling salesperson problem in the context of parameterised complexity. For the generalised minimum spanning tree problem, we analyse the two approaches presented by Hu and Raidl (2012) with respect to the number of clusters that distinguish each other by the chosen representation of possible solutions. Our results show that a (1+1) evolutionary algorithm working with the spanning nodes representation is not a fixed-parameter evolutionary algorithm for the problem, whereas the problem can be solved in fixed-parameter time with the global structure representation. We present hard instances for each approach and show that the two approaches are highly complementary by proving that they solve each other's hard instances very efficiently. For the generalised travelling salesperson problem, we analyse the problem with respect to the number of clusters in the problem instance. Our results show that a (1+1) evolutionary algorithm working with the global structure representation is a fixed-parameter evolutionary algorithm for the problem.
Equivalent electric circuit modeling of PV devices is widely used to predict PV electrical performance. The first task in using the model to calculate the electrical characteristics of a PV device is to find the model...
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Equivalent electric circuit modeling of PV devices is widely used to predict PV electrical performance. The first task in using the model to calculate the electrical characteristics of a PV device is to find the model parameters which represent the PV device. In the present work, parameter estimation for the model parameter using various evolutionary algorithms is presented and compared. The constraint set on the estimation process is that only the data directly available in module datasheets can be used for estimating the parameters. The electrical model accuracy using the estimated parameters is then compared to several electrical models reported in literature for various PV cell technologies. (C) 2013 Elsevier B.V. All rights reserved.
In this work, an efficient design of multiplier-less digital finite impulse response (FIR) filter is presented, where the sub-expression elimination (SE) algorithms are employed on filter coefficients, and optimizatio...
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In this work, an efficient design of multiplier-less digital finite impulse response (FIR) filter is presented, where the sub-expression elimination (SE) algorithms are employed on filter coefficients, and optimization is done with evolutionary algorithms. This FIR filter is designed with novelty of optimizing the quantized coefficients inside each of the respective optimization algorithm, instead of using two separate algorithms: one for generation of optimal continuous coefficients, and second for optimizing the quantized coefficients. Comparative analysis using different SE techniques have been utilized for reducing the requirement of adders on both binary represented and canonic signed digit converted filter coefficients. The simulation results illustrate the impact of proposed algorithm along with significant reduction in number of adders.
Cellular manufacturing is well known as an effective way to improve workshop performances. There are various methods to design cells. Most of them do not take into account constraints specific to the workshop. We are ...
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Cellular manufacturing is well known as an effective way to improve workshop performances. There are various methods to design cells. Most of them do not take into account constraints specific to the workshop. We are interested in the design of manufacturing cells that can take into account specific constraints (for example, certain machines may have to stay together in the same cell because they will share a common resource or certain machines may have to be separated because they will produce interferences). The proposed method uses evolutionary algorithms. The initial solutions are created with an algorithm based on a random tree search and then the solutions evolve thanks to operators designed so as to satisfy, at any stage, the constraints. The suggested method is illustrated through a problem with a known optimum in order to verify that the evolutionary algorithm can find the optimum solution. (C) 2000 Elsevier Science B.V. All rights reserved.
A comparison is made of the behaviour of some evolutionary algorithms in time-varying adaptive recursive filter systems. Simulations show that an algorithm including random immigrants outperforms a more conventional a...
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A comparison is made of the behaviour of some evolutionary algorithms in time-varying adaptive recursive filter systems. Simulations show that an algorithm including random immigrants outperforms a more conventional algorithm using the breeder genetic algorithm as the mutation operator when the time variation is discontinuous, but neither algorithms performs well when the time variation is rapid but smooth. To meet this deficit, a new hybrid algorithm which uses a hill climber as an additional genetic operator, applied for several steps at each generation, is introduced. A comparison is made of the effect of applying the hill climbing operator a few times to all members of the population or a larger number of times solely to the best individual;it is found that applying to the whole population yields the better results, substantially improved compared with those obtained using earlier methods.
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