Culturizing differential evolution for constrained optimization

Becerra, Ricardo Landa; Coello, Carlos A. Coello

doi:10.1109/enc.2004.1342621

Cited by 26 publications

(15 citation statements)

References 13 publications

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“…(ii) each chromosome is compared with the Situational Knowledge homologue chromosome; (iii) if they are equals, the electric component is not changed and is stored in the new individual homologue chromosome of the population, while its parameter value is calculated with a differential evolution technique [22], [23]; (iv) if they are different, the new electrical component and its numerical value are assigned randomly. The update function u s compares the best individual with the current one and, if better, stores it in the domain.…”

Section: B the Cupbesmentioning

confidence: 99%

“…In recent soft computing applications, this problem has been faced by cultural algorithms (CAs) [20]- [23]: during the evolution, the information on the search advance acquired by most promising individual is shared with the entire population of potential solutions. This goal is achieved by means of a dynamic mechanism (belief space) of information selection (best solution acceptance), processing (knowledge update), and diffusion (evolution influence).…”

Section: Introductionmentioning

confidence: 99%

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Automatic Analytical Modeling of EIS Data by Evolutive Programming Based on Cultural Algorithms

Arpaïa

Clemente²,

Zanesco

2006

2006 IEEE Instrumentation and Measurement Technology Conference Proceedings

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Efficiency and accuracy problems in state-of-the-art analytical modeling of electrochemical phenomena through impedance spectroscopy are faced by a Cultural Hybrid Evolutionary Modeling Algorithm (CHEMA). Automatic model definition is improved by an evolutionary program exploiting a solution-search strategy based on a cultural mechanism: information on search advance is transmitted to all potential solutions, rather than only to a small inheriting subset, such as in traditional genetic approach. Experimental results of the proposed approach application to electrochemical impedance spectroscopy for biomedical purposes are presented.

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Section: B the Cupbesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Analytical Modeling of EIS Data by Evolutive Programming Based on Cultural Algorithms

Arpaïa

Clemente²,

Zanesco

2006

2006 IEEE Instrumentation and Measurement Technology Conference Proceedings

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“…Lipmann equation gives the electric charge per unit area of the interface [1,2]: a7I aIVT,P,ui /u -0S where y is the interfacial tension, V is the applied voltage, a is the charge on unit area of the interface, T is the temperature, p is the external pressure and t j I is the chemical potential of the combination of species I, whose net charge is 0. Here, 0(0) is the contact angle without externally applied electrical potential, 0(V) is the contact angle under the electrical potential of V, E is the electric permittivity of the dielectric layer beneath the droplet, Ylg is the interfacial tension between liquid droplet and surrounding gas, and d is the thickness of dielectric layer.…”

Section: The Electrowetting Effectmentioning

confidence: 99%

Theoretical Model and Simulations for the Shape Variation of a Droplet in the Electrowetting Effect

Codreanu

Esinenco

2006

2006 International Semiconductor Conference

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The paper presents a theoretical study and simulations of the variation of the contact angle in the electrowetting phenomenon. We determine the droplet shape based on the fact that when the system is in equilibrium, the energy of the system reaches local minimum. The main parameter that characterizes the droplet shape is the wetting angle. We use a differential evolution algorithm to obtain the value of the wetting angle such that the energy of the system reaches its minimum value. Simulations using the CoventorWare 2005 software have been performed, to study the variation of the droplet shape for different voltage values. The results of the simulations are in good agreement with the theoretical model.

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“…The process of creating new candidates is described in the pseudocode as shown in Fig. 4, [1] [13], [15][16][17]. …”

Section: ) Mutation and Recombination To Create New Vectorsmentioning

confidence: 99%

“…6. [1], [13], [15]− [17]. Generate and evaluate initial population Loop while the termination criteria is not met (e.g.…”

Section: ) Selection and The Overall Dementioning

confidence: 99%

Differential Evolution, an Alternative Approach to Evolutionary Algorithm

Wong

Zhang

Proceedings of the 13th International Conference On, Intelligent Systems Application to Power Systems

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As a relatively new population based optimization technique, differential evolution has been attracting increasing attention for a wide variety of engineering applications including power engineering. Unlike the conventional evolutionary algorithms which depend on predefined probability distribution function for mutation process, differential evolution uses the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently, the object vectors' differences will pass the objective functions topographical information toward the optimization process, and therefore provide more efficient global optimization capability. This paper aims at providing an overview of differential evolution and presenting it as an alternative to evolutionary algorithms with two application examples in power systems.

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Culturizing differential evolution for constrained optimization

Cited by 26 publications

References 13 publications

Automatic Analytical Modeling of EIS Data by Evolutive Programming Based on Cultural Algorithms

Automatic Analytical Modeling of EIS Data by Evolutive Programming Based on Cultural Algorithms

Theoretical Model and Simulations for the Shape Variation of a Droplet in the Electrowetting Effect

Differential Evolution, an Alternative Approach to Evolutionary Algorithm

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