Bibliographies thématiques / Evolutionary computation applications

Littérature scientifique sur le sujet « Evolutionary computation applications »

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Publié le 16 juillet 2024

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Articles de revues sur le sujet "Evolutionary computation applications"

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Hong, Tzung-Pei, Chuan-Kang Ting et Oliver Kramer. « Theory and Applications of Evolutionary Computation ». Applied Computational Intelligence and Soft Computing 2010 (2010) : 1–2. http://dx.doi.org/10.1155/2010/360796.

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Easson, Greg, et H. G. Momm. « Evolutionary Computation for Remote Sensing Applications ». Geography Compass 4, no 3 (mars 2010) : 172–92. http://dx.doi.org/10.1111/j.1749-8198.2009.00309.x.

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Pinho, Jorge, João Luis Sobral et Miguel Rocha. « Parallel evolutionary computation in bioinformatics applications ». Computer Methods and Programs in Biomedicine 110, no 2 (mai 2013) : 183–91. http://dx.doi.org/10.1016/j.cmpb.2012.10.001.

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Cicirello, Vincent A. « Evolutionary Computation : Theories, Techniques, and Applications ». Applied Sciences 14, no 6 (18 mars 2024) : 2542. http://dx.doi.org/10.3390/app14062542.

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Kozlov, AP. « Biological Computation and Compatibility Search in the Possibility Space as the Mechanism of Complexity Increase During Progressive Evolution ». Evolutionary Bioinformatics 18 (janvier 2022) : 117693432211106. http://dx.doi.org/10.1177/11769343221110654.

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The idea of computational processes, which take place in nature, for example, DNA computation, is discussed in the literature. DNA computation that is going on in the immunoglobulin locus of vertebrates shows how the computations in the biological possibility space could operate during evolution. We suggest that the origin of evolutionarily novel genes and genome evolution constitute the original intrinsic computation of the information about new structures in the space of unrealized biological possibilities. Due to DNA computation, the information about future structures is generated and stored in DNA as genetic information. In evolving ontogenies, search algorithms are necessary, which can search for information about evolutionary innovations and morphological novelties. We believe that such algorithms include stochastic gene expression, gene competition, and compatibility search at different levels of structural organization. We formulate the increase in complexity principle in terms of biological computation and hypothesize the possibility of in silico computing of future functions of evolutionarily novel genes.
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Yar, Morteza Husainy, Vahid Rahmati et Hamid Reza Dalili Oskouei. « A Survey on Evolutionary Computation : Methods and Their Applications in Engineering ». Modern Applied Science 10, no 11 (9 août 2016) : 131. http://dx.doi.org/10.5539/mas.v10n11p131.

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Evolutionary computation is now an inseparable branch of artificial intelligence and smart methods based on evolutional algorithms aimed at solving different real world problems by natural procedures involving living creatures. It’s based on random methods, regeneration of data, choosing by changing or replacing data within a system such as personal computer (PC), cloud, or any other data center. This paper briefly studies different evolutionary computation techniques used in some applications specifically image processing, cloud computing and grid computing. These methods are generally categorized as evolutionary algorithms and swarm intelligence. Each of these subfields contains a variety of algorithms and techniques which are presented with their applications. This work tries to demonstrate the benefits of the field by presenting the real world applications of these methods implemented already. Among these applications is cloud computing scheduling problem improved by genetic algorithms, ant colony optimization, and bees algorithm. Some other applications are improvement of grid load balancing, image processing, improved bi-objective dynamic cell formation problem, robust machine cells for dynamic part production, integrated mixed-integer linear programming, robotic applications, and power control in wind turbines.
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Doerr, Benjamin, et Thomas Jansen. « Theory of Evolutionary Computation ». Algorithmica 59, no 3 (9 novembre 2010) : 299–300. http://dx.doi.org/10.1007/s00453-010-9472-3.

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Zhang, Biaobiao, Yue Wu, Jiabin Lu et K. L. Du. « Evolutionary Computation and Its Applications in Neural and Fuzzy Systems ». Applied Computational Intelligence and Soft Computing 2011 (2011) : 1–20. http://dx.doi.org/10.1155/2011/938240.

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Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.
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Holmes, John H. « Methods and applications of evolutionary computation in biomedicine ». Journal of Biomedical Informatics 49 (juin 2014) : 11–15. http://dx.doi.org/10.1016/j.jbi.2014.05.008.

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CHOPARD, BASTIEN, OLIVIER PICTET et MARCO TOMASSINP. « PARALLEL AND DISTRIBUTED EVOLUTIONARY COMPUTATION FOR FINANCIAL APPLICATIONS ». Parallel Algorithms and Applications 15, no 1-2 (juin 2000) : 15–36. http://dx.doi.org/10.1080/01495730008947348.

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Thèses sur le sujet "Evolutionary computation applications"

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Pridgeon, Carey. « Diverse applications of evolutionary computation in bioinformatics : hypermotifs and gene regulatory network inference ». Thesis, University of Exeter, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.479210.

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Abraham, Ajith 1968. « Hybrid soft computing : architecture optimization and applications ». Monash University, Gippsland School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8676.

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Rahman, Izaz Ur. « Novel particle swarm optimization algorithms with applications in power systems ». Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12219.

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Optimization problems are vital in physical sciences, commercial and finance matters. In a nutshell, almost everyone is the stake-holder in certain optimization problems aiming at minimizing the cost of production and losses of system, and also maximizing the profit. In control systems, the optimal configuration problems are essential that have been solved by various newly developed methods. The literature is exhaustively explored for an appropriate optimization method to solve such kind of problems. Particle Swarm Optimization is found to be one of the best among several optimization methods by analysing the experimental results. Two novel PSO variants are introduced in this thesis. The first one is named as N State Markov Jumping Particle Swarm Optimization, which is based on the stochastic technique and Markov chain in updating the particle velocity. We have named the second variant as N State Switching Particle Swarm Optimization, which is based on the evolutionary factor information for updating the velocity. The proposed algorithms are then applied to some widely used mathematical benchmark functions. The statistical results of 30 independent trails illustrate the robustness and accuracy of the proposed algorithms for most of the benchmark functions. The better results in terms of mean minimum evaluation errors and the shortest computation time are illustrated. In order to verify the satisfactory performance and robustness of the proposed algorithms, we have further formulated some basic applications in power system operations. The first application is about the static Economic Load Dispatch and the second application is on the Dynamic Economic Load Dispatch. These are highly complex and non-linear problems of power system operations consisting of various systems and generator constraints. Basically, in the static Economic Load Dispatch, a single load is considered for calculating the cost function. In contrast, the Dynamic Economic Load Dispatch changes the load demand for the cost function dynamically with time. In such a challenging and complex environment the proposed algorithms can be applied. The empirical results obtained by applying both of the proposed methods have substantiated their adaptability and robustness into the real-world environment. It is shown in the numerical results that the proposed algorithms are robust and accurate as compared to the other algorithms. The proposed algorithms have produced consistent best values for their objectives, where satisfying all constraints with zero penalty.
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Kok, Jonathan. « Design methodologies and architectures of hardware-based evolutionary algorithms for aerospace optimisation applications on FPGAS ». Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/72904/5/Jonathan_Kok_Thesis.pdf.

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This thesis is a study of new design methods for allowing evolutionary algorithms to be more effectively utilised in aerospace optimisation applications where computation needs are high and computation platform space may be restrictive. It examines the applicability of special hardware computational platforms known as field programmable gate arrays and shows that with the right implementation methods they can offer significant benefits. This research is a step forward towards the advancement of efficient and highly automated aircraft systems for meeting compact physical constraints in aerospace platforms and providing effective performance speedups over traditional methods.
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Ranjeet, Tirtha. « Coevolutionary algorithms for the optimization of strategies for red teaming applications ». Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2012. https://ro.ecu.edu.au/theses/558.

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Red teaming (RT) is a process that assists an organization in finding vulnerabilities in a system whereby the organization itself takes on the role of an “attacker” to test the system. It is used in various domains including military operations. Traditionally, it is a manual process with some obvious weaknesses: it is expensive, time-consuming, and limited from the perspective of humans “thinking inside the box”. Automated RT is an approach that has the potential to overcome these weaknesses. In this approach both the red team (enemy forces) and blue team (friendly forces) are modelled as intelligent agents in a multi-agent system and the idea is to run many computer simulations, pitting the plan of the red team against the plan of blue team. This research project investigated techniques that can support automated red teaming by conducting a systematic study involving a genetic algorithm (GA), a basic coevolutionary algorithm and three variants of the coevolutionary algorithm. An initial pilot study involving the GA showed some limitations, as GAs only support the optimization of a single population at a time against a fixed strategy. However, in red teaming it is not sufficient to consider just one, or even a few, opponent‟s strategies as, in reality, each team needs to adjust their strategy to account for different strategies that competing teams may utilize at different points. Coevolutionary algorithms (CEAs) were identified as suitable algorithms which were capable of optimizing two teams simultaneously for red teaming. The subsequent investigation of CEAs examined their performance in addressing the characteristics of red teaming problems, such as intransitivity relationships and multimodality, before employing them to optimize two red teaming scenarios. A number of measures were used to evaluate the performance of CEAs and in terms of multimodality, this study introduced a novel n-peak problem and a new performance measure based on the Circular Earth Movers‟ Distance. Results from the investigations involving an intransitive number problem, multimodal problem and two red teaming scenarios showed that in terms of the performance measures used, there is not a single algorithm that consistently outperforms the others across the four test problems. Applications of CEAs on the red teaming scenarios showed that all four variants produced interesting evolved strategies at the end of the optimization process, as well as providing evidence of the potential of CEAs in their future application in red teaming. The developed techniques can potentially be used for red teaming in military operations or analysis for protection of critical infrastructure. The benefits include the modelling of more realistic interactions between the teams, the ability to anticipate and to counteract potentially new types of attacks as well as providing a cost effective solution.
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Creaser, Paul. « Application of evolutionary computation techniques to missile guidance ». Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.

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Sharifi, Soroosh. « Application of evolutionary computation to open channel flow modelling ». Thesis, University of Birmingham, 2009. http://etheses.bham.ac.uk//id/eprint/478/.

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This thesis examines the application of two evolutionary computation techniques to two different aspects of open channel flow. The first part of the work is concerned with evaluating the ability of an evolutionary algorithm to provide insight and guidance into the correct magnitude and trend of the three parameters required in order to successfully apply a quasi 2D depth averaged Reynolds Averaged Navier Stokes (RANS) model to the flow in prismatic open channels. The RANS modeled adopted is the Shiono Knight Method (SKM) which requires three input parameters in order to provide closure, i.e. the friction factor (\(f\)), dimensionless eddy viscosity (λ) and a sink term representing the effects of secondary flow (Γ). A non-dominated sorting genetic algorithm II (NSGA-II) is used to construct a multiobjective evolutionary based calibration framework for the SKM from which conclusions relating to the appropriate values of \(f\), λ and Γ are made. The framework is applied to flows in homogenous and heterogeneous trapezoidal channels, homogenous rectangular channels and a number of natural rivers. The variation of \(f\), λ and Γ with the wetted parameter ratio (\(P_b\)/\(P_w\)) and panel structure for a variety of situations is investigated in detail. The situation is complex: \(f\) is relatively independent of the panel structure but is shown to vary with P\(_b\)/P\(_w\), the values of λ and Γ are highly affected by the panel structure but λ is shown to be relatively insensitive to changes in \(P_b\)/\(P_w\). Appropriate guidance in the form of empirical equations are provided. Comparing the results to previous calibration attempts highlights the effectiveness of the proposed semi-automated framework developed in this thesis. The latter part of the thesis examines the possibility of using genetic programming as an effective data mining tool in order to build a model induction methodology. To this end the flow over a free overfall is exampled for a variety of cross section shapes. In total, 18 datasets representing 1373 experiments were interrogated. It was found that an expression of form \(h_c\)=A\(h_e\)\(^{B\sqrt S_o}\), where \(h_c\) is the critical depth, \(h_e\) is the depth at the brink, \(S_o\) is the bed slope and A and B are two cross section dependant constants, was valid regardless of cross sectional shape and Froude number. In all of the cases examined this expression fitted the data to within a coefficient of determination (CoD) larger than 0.975. The discovery of this single expression for all datasets represents a significant step forward and highlights the power and potential of genetic programming.
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Hayward, Kevin. « Application of evolutionary algorithms to engineering design ». University of Western Australia. School of Mechanical Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0018.

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The efficiency of the mechanical design process can be improved by the use of evolutionary algorithms. Evolutionary algorithms provide a convenient and robust method to search for appropriate design solutions. Difficult non-linear problems are often encountered during the mechanical engineering design process. Solutions to these problems often involve computationally-intensive simulations. Evolutionary algorithms tuned to work with a small number of solution iterations can be used to automate the search for optimal solutions to these problems. An evolutionary algorithm was designed to give reliable results after a few thousand iterations; additionally the scalability and the ease of application to varied problems were considered. Convergence velocity of the algorithm was improved considerably by altering the mutation-based parameters in the algorithm. Much of this performance gain can be attributed to making the magnitude of the mutation and the minimum mutation rates self-adaptive. Three motorsport based design problems were simulated and the evolutionary algorithm was applied to search for appropriate solutions. The first two, a racing-line generator and a suspension kinematics simulation, were investigated to highlight properties of the evolutionary algorithm: reliability; solution representation; determining variable/performance relationships; and multiple objectives were discussed. The last of these problems was the lap-time simulation of a Formula SAE vehicle. This problem was solved with 32 variables, including a number of major conceptual differences. The solution to this optimisation was found to be significantly better than the 2004 UWA Motorsport vehicle, which finished 2nd in the 2005 US competition. A simulated comparison showed the optimised vehicle would score 62 more points (out of 675) in the dynamic events of the Formula SAE competition. Notably the optimised vehicle had a different conceptual design to the actual UWA vehicle. These results can be used to improve the design of future Formula SAE vehicles. The evolutionary algorithm developed here can be used as an automated search procedure for problems where performance solutions are computationally intensive.
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Wiltshire, Serge William. « On The Application Of Computational Modeling To Complex Food Systems Issues ». ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1077.

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Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool.
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Antão, Tiago Rodrigues. « Evolutionary applications of population genetics with a focus on malaria : a computational approach ». Thesis, University of Liverpool, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569901.

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Malaria is a major public health concern for the one-third of the human population esti- mated to be exposed to the threat of the most virulent species, Plasmodium falciparum. Modern molecular and computational tools from population genetics may help to better understand and fight the burden of drug resistant malaria. Malaria biology is substantially different from the underlying paradigm of standard population genetics models, most notably because malaria has both a asexual haploid phase and a sexual diploid phase where selfing (i.e. mating between genetically identical parasites) is possible. It is therefore fundamental to understand if commonly used population genetics methods are robust to the deviations from standard expectations imposed by the malaria life-cycle. We build novel models of malaria population genetics and provide guidelines to interpret empirical studies of the spread of drug resistance. Using realistic models of epistasis between genes involved in drug resistance we suggest that all signals of linkage disequilibrium (LD) are possible and that researchers should be confident in reporting a lack of statistical association between genes involved in resistance to antimalarials. We also suggest that researchers should be cautious in interpreting changes in the prevalence of drug resistance after control interventions as reductions in transmission can cause a change in prevalence without concomitant change in frequency of resistance. We provide guidelines to better design and interpret studies related to estimating effective population size (Ne). We use computational simulations to study scenarios that can approximate control and elimination interventions (i.e. where a significant part of the population is killed). For Ne estimation our results suggest that researchers must increase the number of individuals and loci genotyped in order to have sufficiently precise Ne estimates. LD-based Ne estimators are more appropriate for early detection of control and elimination interventions than temporal-based Ne estimators. Long- term estimators based on heterozygosity should not be used to make inferences about contemporary demographic processes. We also applied our analysis to disease vectors and we concluded that LD-based estimation is able to detect demographic seasonality patterns (i.e. changes in population size due to variations imposed by wet and dry seasons) whereas temporal estimators will provide averages over longer periods of time. We also studied selection detection using FsT-outlier approaches. Our results suggest that temporal FST might not be appropriate for early detection of genes involved in drug resistance (e.g. in the case of artesunate derivatives). We also provide software and guidelines to better design and interpret studies (also across other taxa) of selection based on FsT-outlier approaches. Most notably our results suggest that sampling only one or two SNPs per locus (as it is done in many empirical studies) might not be sufficient to detect areas of the genome under selection, and that at least 4 SNPs per loci should be genotyped.
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Livres sur le sujet "Evolutionary computation applications"

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Castillo, Pedro A., et Juan Luis Jiménez Laredo, dir. Applications of Evolutionary Computation. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72699-7.

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Jiménez Laredo, Juan Luis, J. Ignacio Hidalgo et Kehinde Oluwatoyin Babaagba, dir. Applications of Evolutionary Computation. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02462-7.

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Sim, Kevin, et Paul Kaufmann, dir. Applications of Evolutionary Computation. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77538-8.

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Mora, Antonio M., et Giovanni Squillero, dir. Applications of Evolutionary Computation. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16549-3.

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Squillero, Giovanni, et Kevin Sim, dir. Applications of Evolutionary Computation. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55792-2.

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Squillero, Giovanni, et Kevin Sim, dir. Applications of Evolutionary Computation. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55849-3.

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Di Chio, Cecilia, Alexandros Agapitos, Stefano Cagnoni, Carlos Cotta, Francisco Fernández de Vega, Gianni A. Di Caro, Rolf Drechsler et al., dir. Applications of Evolutionary Computation. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29178-4.

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Di Chio, Cecilia, Anthony Brabazon, Gianni A. Di Caro, Rolf Drechsler, Muddassar Farooq, Jörn Grahl, Gary Greenfield et al., dir. Applications of Evolutionary Computation. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20520-0.

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Di Chio, Cecilia, Stefano Cagnoni, Carlos Cotta, Marc Ebner, Anikó Ekárt, Anna I. Esparcia-Alcázar, Juan J. Merelo et al., dir. Applications of Evolutionary Computation. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20525-5.

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Esparcia-Alcázar, Anna I., et Antonio M. Mora, dir. Applications of Evolutionary Computation. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45523-4.

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Chapitres de livres sur le sujet "Evolutionary computation applications"

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Mitra, Tamoghna, Henrik SaxÉN et Nirupam Chakraborti. « Chapter 4 Evolutionary Algorithms In Ironmaking Applications ». Dans Evolutionary Computation, 81–112. 3333 Mistwell Crescent, Oakville, ON L6L 0A2, Canada : Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388-5.

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Singru, Pravin M., Vishal Jain, Nikilesh Krishnakumar, A. Garg, K. Tai et V. Vijayaraghavan. « Chapter 7 Gene Expression Programming In Nanotechnology Applications ». Dans Evolutionary Computation, 195–210. 3333 Mistwell Crescent, Oakville, ON L6L 0A2, Canada : Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388-8.

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Coello Coello, Carlos A., David A. Van Veldhuizen et Gary B. Lamont. « Applications ». Dans Genetic Algorithms and Evolutionary Computation, 207–92. Boston, MA : Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5184-0_6.

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Trivedi, Vibhu, et Manojkumar Ramteke. « Chapter 2 Bio-Mimetic Adaptations Of Genetic Algorithm And Their Applications To Chemical Engineering ». Dans Evolutionary Computation, 21–54. 3333 Mistwell Crescent, Oakville, ON L6L 0A2, Canada : Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388-3.

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Zhang, Gexiang, Mario J. Pérez-Jiménez et Marian Gheorghe. « Fundamentals of Evolutionary Computation ». Dans Real-life Applications with Membrane Computing, 11–32. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55989-6_2.

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Howard, D. « Bioinformatics with Evolutionary Computation ». Dans Real World Applications of Computational Intelligence, 245–81. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11364160_8.

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Whigham, P. A., et G. B. Fogel. « Ecological Applications of Evolutionary Computation ». Dans Ecological Informatics, 49–71. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05150-4_4.

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Gulisano, Vincenzo, et Eric Medvet. « Evolutionary Computation Meets Stream Processing ». Dans Applications of Evolutionary Computation, 377–93. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56852-7_24.

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Kawulok, Michal, Pawel Benecki, Daniel Kostrzewa et Lukasz Skonieczny. « Towards Evolutionary Super-Resolution ». Dans Applications of Evolutionary Computation, 480–96. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77538-8_33.

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Yaman, Anil, Ahmed Hallawa, Matt Coler et Giovanni Iacca. « Presenting the ECO : Evolutionary Computation Ontology ». Dans Applications of Evolutionary Computation, 603–19. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55849-3_39.

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Actes de conférences sur le sujet "Evolutionary computation applications"

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Smith, Stephen L. « Medical Applications of Evolutionary Computation ». Dans GECCO '15 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2015. http://dx.doi.org/10.1145/2739482.2756567.

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Smith, Stephen L. « Medical applications of evolutionary computation ». Dans GECCO '14 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2014. http://dx.doi.org/10.1145/2598394.2605364.

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Smith, Stephen L. « Medical applications of evolutionary computation ». Dans GECCO '18 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2018. http://dx.doi.org/10.1145/3205651.3207873.

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Smith, Stephen L. « Medical Applications of Evolutionary Computation ». Dans GECCO '16 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2016. http://dx.doi.org/10.1145/2908961.2926997.

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Julstrom, Bryant A. « Session details : Applications of evolutionary computation ». Dans SAC '08 : The 2008 ACM Symposium on Applied Computing. New York, NY, USA : ACM, 2008. http://dx.doi.org/10.1145/3260572.

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Torresen, Jim. « Addressing ethical challenges within evolutionary computation applications ». Dans GECCO '20 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3377929.3389894.

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Julstrom, Bryant A. « Special track on Applications of Evolutionary Computation ». Dans the 2008 ACM symposium. New York, New York, USA : ACM Press, 2008. http://dx.doi.org/10.1145/1363686.1364111.

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Julstrom, Bryant. « Session details : Applications of evolutionary computation track ». Dans SAC09 : The 2009 ACM Symposium on Applied Computing. New York, NY, USA : ACM, 2009. http://dx.doi.org/10.1145/3257380.

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Liu, Ying. « Multiobjective Evolutionary Computation for Market Segmentation ». Dans 13th International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010684400003063.

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« Session details : Biological applications ». Dans GECCO05 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2005. http://dx.doi.org/10.1145/3249398.

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