Relevant bibliographies by topics / Evolutionary Optimiser

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Evolutionary Optimiser'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Evolutionary Optimiser"

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Ab. Rashid, M. F. F., N. M. Z. Nik Mohamed, and A. N. Mohd Rose. "A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing." Journal of Mechanical Engineering and Sciences 13, no. 4 (2019): 5905–21. http://dx.doi.org/10.15282/jmes.13.4.2019.13.0469.

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Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB p
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al-Rifaie, Mohammad Majid. "Exploration and Exploitation Zones in a Minimalist Swarm Optimiser." Entropy 23, no. 8 (2021): 977. http://dx.doi.org/10.3390/e23080977.

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The trade off between exploration and exploitation is one of the key challenges in evolutionary and swarm optimisers which are led by guided and stochastic search. This work investigates the exploration and exploitation balance in a minimalist swarm optimiser in order to offer insights into the population’s behaviour. The minimalist and vector-stripped nature of the algorithm—dispersive flies optimisation or DFO—reduces the challenges of understanding particles’ oscillation around constantly changing centres, their influence on one another, and their trajectory. The aim is to examine the popul
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Kunakote, Tawatchai, and Sujin Bureerat. "Surrogate-Assisted Multiobjective Evolutionary Algorithms for Structural Shape and Sizing Optimisation." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/695172.

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The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary optimisation process to enhance the performance of the optimiser when solving design problems with expensive function evaluation. Several surrogate models including quadratic function, radial basis function, neural network, and Kriging models are employed in combination with SPEA2 using real codes. The various hybrid optimisation strategies are implemented on eight simultaneous sh
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Giel, Oliver, and Per Kristian Lehre. "On the Effect of Populations in Evolutionary Multi-Objective Optimisation." Evolutionary Computation 18, no. 3 (2010): 335–56. http://dx.doi.org/10.1162/evco_a_00013.

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Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present two simple bi-objective problems which emphasise when populations are needed. Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm simple evolutionary multi-objective optimiser (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA is suc
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Delelegn, S. W., A. Pathirana, B. Gersonius, A. G. Adeogun, and K. Vairavamoorthy. "Multi-objective optimisation of cost–benefit of urban flood management using a 1D2D coupled model." Water Science and Technology 63, no. 5 (2011): 1053–59. http://dx.doi.org/10.2166/wst.2011.290.

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This paper presents a multi-objective optimisation (MOO) tool for urban drainage management that is based on a 1D2D coupled model of SWMM5 (1D sub-surface flow model) and BreZo (2D surface flow model). This coupled model is linked with NSGA-II, which is an Evolutionary Algorithm-based optimiser. Previously the combination of a surface/sub-surface flow model and evolutionary optimisation has been considered to be infeasible due to the computational demands. The 1D2D coupled model used here shows a computational efficiency that is acceptable for optimisation. This technological advance is the re
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Sartakhti, Moein Salimi, Ahmad Yoosofan, Ali Asghar Fatehi, and Ali Rahimi. "Single Document Summarization Based on Grey Wolf Optimization." Global Journal of Computer Sciences: Theory and Research 10, no. 2 (2020): 48–56. http://dx.doi.org/10.18844/gjcs.v10i2.5807.

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The amazing growth of online services has caused an information explosion issue. Text summarisation is condensing the text into a small version and preserving its overall concept. Text summarisation is an important way to extract significant information from documents and offer that information to the user in an abbreviated form while preserving its major content. For human beings, it is very difficult to summarise large documents. To do this, this paper uses some sentence features and word features. These features assign scores to all the sentences. In this paper, we combine these features by
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Marrero, Alejandro, Eduardo Segredo, Coromoto León, and Carlos Segura. "A Memetic Decomposition-Based Multi-Objective Evolutionary Algorithm Applied to a Constrained Menu Planning Problem." Mathematics 8, no. 11 (2020): 1960. http://dx.doi.org/10.3390/math8111960.

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Encouraging healthy and balanced diet plans is one of the most important action points for governments around the world. Generating healthy, balanced and inexpensive menu plans that fulfil all the recommendations given by nutritionists is a complex and time-consuming task; because of this, computer science has an important role in this area. This paper deals with a novel constrained multi-objective formulation of the menu planning problem specially designed for school canteens that considers the minimisation of the cost and the minimisation of the level of repetition of the specific courses an
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Ashrafian, Ali, Naser Safaeian Hamzehkolaei, Ngakan Ketut Acwin Dwijendra, and Maziar Yazdani. "An Evolutionary Neuro-Fuzzy-Based Approach to Estimate the Compressive Strength of Eco-Friendly Concrete Containing Recycled Construction Wastes." Buildings 12, no. 8 (2022): 1280. http://dx.doi.org/10.3390/buildings12081280.

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There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D waste, especially in concretes used in construction, further environmental damage can be prevented. By using these concretes, energy consumption and environmental impacts of concrete production can be reduced. The behaviour of these types of concrete in laboratories has been extensively studied, but reliable methods for estimating their behaviour bas
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Gonzalez, L. F., D. S. Lee, K. Srinivas, and K. C. Wong. "Single and multi–objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm." Aeronautical Journal 110, no. 1112 (2006): 659–72. http://dx.doi.org/10.1017/s0001924000001524.

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Abstract Unmanned aerial vehicle (UAV) design tends to focus on sensors, payload and navigation systems, as these are the most expensive components. One area that is often overlooked in UAV design is airframe and aerodynamic shape optimisation. As for manned aircraft, optimisation is important in order to extend the operational envelope and efficiency of these vehicles. A traditional approach to optimisation is to use gradient-based techniques. These techniques are effective when applied to specific problems and within a specified range. These methods are efficient for finding optimal global s
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Saravanan, R., S. Ramabalan, and C. Balamurugan. "Multiobjective trajectory planner for industrial robots with payload constraints." Robotica 26, no. 6 (2008): 753–65. http://dx.doi.org/10.1017/s0263574708004359.

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SUMMARYA general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select
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Dissertations / Theses on the topic "Evolutionary Optimiser"

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Damp, Lloyd Hollis. "Multi-Objective and Multidisciplinary Design Optimisation of Unmanned Aerial Vehicle Systems using Hierarchical Asynchronous Parallel Multi-Objective Evolutionary Algorithms." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/1858.

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The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optim
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Damp, Lloyd Hollis. "Multi-Objective and Multidisciplinary Design Optimisation of Unmanned Aerial Vehicle Systems using Hierarchical Asynchronous Parallel Multi-Objective Evolutionary Algorithms." University of Sydney, 2007. http://hdl.handle.net/2123/1858.

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Master of Engineering (Research)<br>The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The
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Lease, Basil Andy. "Weed/Plant Classification Using Evolutionary Optimised Ensemble Based On Local Binary Patterns." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/88106.

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This thesis presents a novel pixel-level weed classification through rotation-invariant uniform local binary pattern (LBP) features for precision weed control. Based on two-level optimisation structure; First, Genetic Algorithm (GA) optimisation to select the best rotation-invariant uniform LBP configurations; Second, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in the Neural Network (NN) ensemble to select the best combinations of voting weights of the predicted outcome for each classifier. The model obtained 87.9% accuracy in CWFID public benchmark.
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Kaylani, Assem. "AN ADAPTIVE MULTIOBJECTIVE EVOLUTIONARY APPROACH TO OPTIMIZE ARTMAP NEURAL NETWORKS." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2538.

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This dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing the size and the generalization performance of the ART neural network. A number of researchers have foc
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Guan, C. "Evolutionary and swarm algorithm optimized density-based clustering and classification for data analytics." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3021212/.

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Clustering is one of the most widely used pattern recognition technologies for data analytics. Density-based clustering is a category of clustering methods which can find arbitrary shaped clusters. A well-known density-based clustering algorithm is Density- Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN has three drawbacks: firstly, the parameters for DBSCAN are hard to set; secondly, the number of clusters cannot be controlled by the users; and thirdly, DBSCAN cannot directly be used as a classifier. With addressing the drawbacks of DBSCAN, a novel framework, Evolutionar
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White, William E. "Use of Empirically Optimized Perturbations for Separating and Characterizing Pyloric Neurons." Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1368055391.

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Fong, Kwong Fai. "Optimized design and energy management of heating, ventilating and air conditioning systems by evolutionary algorithm." Thesis, De Montfort University, 2006. http://hdl.handle.net/2086/5216.

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Lakshminarayanan, Srivathsan. "Nature Inspired Grey Wolf Optimizer Algorithm for Minimizing Operating Cost in Green Smart Home." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1438102173.

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Ferreira, David. "Résistance au stress lors de la phase de latence en fermentation œnologique et développement de levures optimisées." Thesis, Montpellier, SupAgro, 2017. http://www.theses.fr/2017NSAM0051.

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Résumé : Saccharomyces cerevisiae, utilisée depuis des millénaires pour la fermentation du vin du fait de son endurance et de ses qualités inégalables, est de nos jours largement utilisée pour inoculer les mouts de raisin. Néanmoins, lors de l'inoculation, les souches oenologiques doivent faire face à des stress spécifiques qui peuvent compromettre le début de la fermentation. L’objectif de ce travail est d'élucider les bases métaboliques et moléculaires de la résistance multi-stress pendant la phase de latence en conditions oenologiques. Nous avons tout d'abord caractérisé un ensemble de levu
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Cheng, Yo-Hao, and 鄭又豪. "Application of Interactive Evolutionary Algorithms to Optimize Multimedia Mobile Advertising Problems." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/64245044542540829058.

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碩士<br>高苑科技大學<br>資訊科技應用研究所<br>101<br>Mobile marketing and advertising for specific consumer groups different time periods and regions associated effective advertising, is a new type of mobile ad can be customized mobile ad is targeted customer base would like to know the correctrelevant and valuable even be allowed in advance commercial information Main object of study for the promotional message of supermarkets in Taiwan mobile applications, mobile ads which many advertising messages, promotional messages for each grade produce the maximum effect is no way of knowing whether consumers to disc
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Books on the topic "Evolutionary Optimiser"

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Ruse, Michael. Moving Forward. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190867577.003.0012.

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The Augustinian vision of humankind, on which so much Christian thinking about war is based, is false. Thanks to Darwinian evolutionary biology we know there was no original couple, Adam and Eve; there was no eating of the apple; there is no original sin. We are not innately depraved in this way. Morbid fatalism is inappropriate. The killer-ape vision of humankind, on which so much Darwinian thinking about war is based, is equally false. Thanks to updated Darwinian evolutionary biology, we know that we did not evolve in the violent ways often presumed, and that in major respects we are designe
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Depoorter, Ben, and Paul H. Rubin. Judge-Made Law and the Common Law Process. Edited by Francesco Parisi. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199684250.013.001.

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One of the most illustrious normative claims in the law and economics literature, originating with Posner and supported by models of evolutionary legal change, posits that a system of judge-made law offers efficiency advantages over statute-based systems. In recent years, however, scholarship has identified aspects of common law systems that undermine the optimism about judge-made efficiency. This chapter reviews the original economic literature on the efficiency of the common law and then describes supply- and demand-side obstacles to efficient judge-made law. On the supply side, a rich body
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May, Joshua. Regard for Reason in the Moral Mind. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198811572.001.0001.

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The burgeoning science of ethics has produced a trend toward pessimism. Ordinary moral judgment and motivation, we’re told, are profoundly influenced by arbitrary factors and ultimately driven by unreasoned feelings or emotions—fertile ground for sweeping debunking arguments. This book counters the current orthodoxy on its own terms by carefully engaging with the empirical literature. The resulting view, optimistic rationalism, maintains that reason plays a pervasive role in our moral minds and that ordinary moral reasoning is not particularly flawed or in need of serious repair. The science d
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Butz, Martin V., and Esther F. Kutter. Cognitive Science is Interdisciplinary. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0002.

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Cognitive Science has come a long way – even the old Greeks have put forward intricate thoughts about what our minds can know and what seems inaccessible. This chapter addresses the historical development of cognitive science. The fundamental homunculus and qualia problems are introduced, and tentative answers from embodied cognitive science are sketched-out. A look at biology emphasizes that our bodies and brains are evolutionarily shaped, making us ready to develop our minds. Psychologically, minds develop and adapt given environmental feedback, striving to optimize behavior. Only by means o
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Butz, Martin V., and Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.

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For more than 2000 years Greek philosophers have thought about the puzzling introspectively assessed dichotomy between our physical bodies and our seemingly non-physical minds. How is it that we can think highly abstract thoughts, seemingly fully detached from actual, physical reality? Despite the obvious interactions between mind and body (we get tired, we are hungry, we stay up late despite being tired, etc.), until today it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. Despite a big movement towards embodied cognitive science over the last 20
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Book chapters on the topic "Evolutionary Optimiser"

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Badar, Altaf Q. H. "Grey Wolf Optimizer." In Evolutionary Optimization Algorithms. CRC Press, 2021. http://dx.doi.org/10.1201/9781003206477-8.

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Oh, Sung-Kwun, Byoung-Jun Park, Witold Pedrycz, and Hyun-Ki Kim. "Evolutionally Optimized Fuzzy Neural Networks Based on Evolutionary Fuzzy Granulation." In Computational Science and Its Applications – ICCSA 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424925_93.

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al-Rifaie, Mohammad Majid, and Tim Blackwell. "Swarm Optimised Few-View Binary Tomography." In Applications of Evolutionary Computation. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02462-7_3.

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Sanchez, Ernesto, Massimiliano Schillaci, and Giovanni Squillero. "Why yet another one evolutionary optimizer?" In Evolutionary Optimization: the µGP toolkit. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09426-7_2.

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Kováč, Ladislav. "Third Movement. The Ultimate Optimism: Finitics." In SpringerBriefs in Evolutionary Biology. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20660-8_3.

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Si, Tapas, and Biplab Mandal. "Opposition Based Particle Swarm Optimizer with Ring Topology." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_54.

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Cagnina, Leticia, Susana Esquivel, and Carlos A. Coello Coello. "Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study." In Evolutionary Scheduling. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48584-1_6.

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Scott, Cathy, Neil Urquhart, and Emma Hart. "Influence of Topology and Payload on CO2 Optimised Vehicle Routing." In Applications of Evolutionary Computation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12242-2_15.

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Ross, Peter, and Andrew Tuson. "Directing the search of evolutionary and neighbourhood-search optimisers for the flowshop sequencing problem with an idle-time heuristic." In Evolutionary Computing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0027176.

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Falcón-Cardona, Jesús Guillermo, and Carlos A. Coello Coello. "Towards a More General Many-objective Evolutionary Optimizer." In Parallel Problem Solving from Nature – PPSN XV. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99253-2_27.

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Conference papers on the topic "Evolutionary Optimiser"

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Rakitianskaia, Anna, and Andries P. Engelbrecht. "Cooperative charged particle swarm optimiser." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4630908.

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Hughes, Evan J. "MSOPS-II: A general-purpose Many-Objective optimiser." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424985.

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Adra, Salem Fawaz, Ian Griffin, and Peter J. Fleming. "An informed convergence accelerator for evolutionary multiobjective optimiser." In the 9th annual conference. ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277110.

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Tkach, Itshak, and Tim Blackwell. "Measuring optimiser performance on a conical barrier tree benchmark." In GECCO '22: Genetic and Evolutionary Computation Conference. ACM, 2022. http://dx.doi.org/10.1145/3512290.3528842.

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Sabar, Nasser R., Ayad Turky, and Andy Song. "Adaptive Multi-optimiser Cooperative Co-evolution for Large-Scale Optimisation." In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. http://dx.doi.org/10.1109/cec.2019.8790022.

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Poli, Riccardo, Dan Bratton, Tim Blackwell, and Jim Kennedy. "Theoretical derivation, analysis and empirical evaluation of a simpler Particle Swarm Optimiser." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424713.

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al-Rifaie, Mohammad Majid. "Investigating Knowledge-Based Exploration-Exploitation Balance in a Minimalist Swarm Optimiser." In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021. http://dx.doi.org/10.1109/cec45853.2021.9504805.

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Fieldsend, Jonathan E. "Running Up Those Hills: Multi-modal search with the niching migratory multi-swarm optimiser." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900309.

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Sakal, James, Jonathan E. Fieldsend, and Edward Keedwell. "Learning assignment order in an ant colony optimiser for the university course timetabling problem." In GECCO '21: Genetic and Evolutionary Computation Conference. ACM, 2021. http://dx.doi.org/10.1145/3449726.3459534.

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Lee, DongSeop, Jacques Periaux, and Luis Felipe Gonzalez. "UAS Mission Path Planning System (MPPS) Using Hybrid-Game Coupled to Multi-Objective Optimiser." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86749.

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This paper presents the application of advanced optimization techniques to Unmanned Aerial Systems (UAS) Mission Path Planning System (MPPS) using Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and a Hybrid Game strategy are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The resulting trajectories on a three-dimension terrain are collision-free and are represented by using Be´zier spline curves from start position to targe
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Reports on the topic "Evolutionary Optimiser"

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate th
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McElwain, Terry F., Eugene Pipano, Guy H. Palmer, Varda Shkap, Stephn A. Hines, and Wendy C. Brown. Protection of Cattle against Babesiosis: Immunization against Babesia bovis with an Optimized RAP-1/Apical Complex Construct. United States Department of Agriculture, 1999. http://dx.doi.org/10.32747/1999.7573063.bard.

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Previous research and current efforts at control of babesiosis fall short of meeting the needs of countries where the disease is endemic, such as Israel, as well as the needs of exporting countries and countries bordering on endemic areas, such as the U.S. Our long-term goal is to develop improved methods of immunization against bovine babesiosis based on an understanding of the molecular mechanisms of immune protection and parasite targets of a protective immune response. In our previous BARD project, we established the basis for focusing on rhoptry antigens as components of a subunit vaccine
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