Relevant bibliographies by topics / Evolutionary Optimiser

Academic literature on the topic 'Evolutionary Optimiser'

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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 (December 30, 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 problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.
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al-Rifaie, Mohammad Majid. "Exploration and Exploitation Zones in a Minimalist Swarm Optimiser." Entropy 23, no. 8 (July 29, 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 population’s dimensional behaviour in each iteration and each defined exploration-exploitation zone, and to subsequently offer improvements to the working of the optimiser. The derived variants, titled unified DFO or uDFO, are successfully applied to an extensive set of test functions, as well as high-dimensional tomographic reconstruction, which is an important inverse problem in medical and industrial imaging.
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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 shape and sizing design problems of structures taking into account of structural weight, lateral bucking, natural frequency, and stress. Structural analysis is carried out by using a finite element procedure. The optimum results obtained are compared and discussed. The performance assessment is based on the hypervolume indicator. The performance of the surrogate models for estimating design constraints is investigated. It has been found that, by using a quadratic function surrogate model, the optimiser searching performance is greatly improved.
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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 (September 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 successful and all other algorithms fail.
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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 (March 1, 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 result of the application of a triangular irregular discretisation process and an explicit finite volume solver in the 2D surface flow model. Besides that, OpenMP based parallelisation was employed at optimiser level to further improve the computational speed of the MOO tool. The MOO tool has been applied to an existing sewer network in West Garforth, UK. This application demonstrates the advantages of using multi-objective optimisation by providing an easy-to-comprehend Pareto-optimal front (relating investment cost to expected flood damage) that could be used for decision making processes, without repeatedly going through the modelling–optimisation stage.
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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 (October 30, 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 Grey Wolf Optimiser (GWO). Optimisation of features gives better results than using individual features. This is the first attempt to show the performance of GWO for Persian text summarisation. The proposed method is compared with the genetic algorithm and the evolutionary strategy. The results show that our model will be useful in this research area. Keywords: Text summarisation, genetic algorithm, sentence, score function, evolutionary strategy.
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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 (November 5, 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 and food groups contained in the plans. Particularly, this paper proposes a multi-objective memetic approach based on the well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D). A crossover operator specifically designed for this problem is included in the approach. Moreover, an ad-hoc iterated local search (ILS) is considered for the improvement phase. As a result, our proposal is referred to as ILS-MOEA/D. A wide experimental comparison against a recently proposed single-objective memetic scheme, which includes explicit mechanisms to promote diversity in the decision variable space, is provided. The experimental assessment shows that, even though the single-objective approach yields menu plans with lower costs, our multi-objective proposal offers menu plans with a significantly lower level of repetition of courses and food groups, with only a minor increase in cost. Furthermore, our studies demonstrate that the application of multi-objective optimisers can be used to implicitly promote diversity not only in the objective function space, but also in the decision variable space. Consequently, in contrast to the single-objective optimiser, there was no need to include an explicit strategy to manage the diversity in the decision space in the case of the multi-objective approach.
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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 (August 21, 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 based on the available data are required. Consequently, this research proposes a hybrid intelligent system, Fuzzy Group Method of Data Handling (GMDH)–Horse herd Optimisation Algorithm (HOA), for predicting one of the most important parameters in concrete structure design, compressive strength. In order to avoid uncertainty in the modelling process, crisp input values were converted to Fuzzy values (Fuzzification). Next, using Fuzzy input variables, the group method of data handling is used to predict the compressive strength of recycled aggregate concrete. The HOA algorithm is one of the newest metaheuristic algorithms being used to optimise the Fuzzy GMDH structure. Several databases containing experimental mix design records containing mixture components are gathered from published documents for compressive strength to assess the accuracy and reliability of the proposed hybrid Fuzzy-based model. Compared to other original approaches, the proposed Fuzzy GMDH model with the HOA optimiser outperformed them in terms of accuracy. A Monte Carlo simulation is also employed for uncertainty analysis of the empirical, standalone, and hybridised models in order to demonstrate that the evolutionary Fuzzy-based approach has less uncertainty than the standalone methods when simulating compressive strength.
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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 (October 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 solutions if the objective functions and constraints are differentiable. If a broader application of the optimiser is desired, or when the complexity of the problem arises because it is multi-modal, involves approximation, is non-differentiable, or involves multiple objectives and physics, as it is often the case in aerodynamic optimisation, more robust and alternative numerical tools are required. Emerging techniques such as evolutionary algorithms (EAs) have been shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions among many local optima, are easily executed in parallel, and can be adapted to arbitrary solver codes without major modifications. In this paper, the formulation and application of a evolutionary technique for aerofoil shape optimisation is described. Initially, the paper presents an introduction to the features of the method and a short discussion on multi-objective optimisation. The method is first illustrated on its application to mathematical test cases. Then it is applied to representative test cases related to aerofoil design. Results indicate the ability of the method for finding optimal solutions and capturing Pareto optimal fronts.
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Saravanan, R., S. Ramabalan, and C. Balamurugan. "Multiobjective trajectory planner for industrial robots with payload constraints." Robotica 26, no. 6 (November 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 the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of the NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MODE are best for this problem.
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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 optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.
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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)
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 optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.
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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 focused on the evolutionary optimization of neural networks, but no research has been performed on the evolutionary optimization of ART neural networks, prior to 2006, when Daraiseh has used evolutionary techniques for the optimization of ART structures. This dissertation extends in many ways and expands in different directions the evolution of ART architectures, such as: (a) uses a multi-objective optimization of ART structures, thus providing to the user multiple solutions (ART networks) with varying degrees of merit, instead of a single solution (b) uses GA parameters that are adaptively determined throughout the ART evolution, (c) identifies a proper size of the validation set used to calculate the fitness function needed for ART's evolution, thus speeding up the evolutionary process, (d) produces experimental results that demonstrate the evolved ART's effectiveness (good accuracy and small size) and efficiency (speed) compared with other competitive ART structures, as well as other classifiers (CART (Classification and Regression Trees) and SVM (Support Vector Machines)). The overall methodology to evolve ART using a multi-objective approach, the chromosome representation of an ART neural network, the genetic operators used in ART's evolution, and the automatic adaptation of some of the GA parameters in ART's evolution could also be applied in the evolution of other exemplar based neural network classifiers such as the probabilistic neural network and the radial basis function neural network.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering PhD
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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, Evolutionary and Swarm Algorithm optimised Density-based Clustering and Classification (ESA-DCC), is proposed. Evolutionary and Swarm Algorithm (ESA), has been applied in various different research fields regarding optimisation problems, including data analytics. Numerous categories of ESAs have been proposed, such as, Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Differential Evaluation (DE) and Artificial Bee Colony (ABC). In this thesis, ESA is used to search the best parameters of density-based clustering and classification in the ESA-DCC framework to address the first drawback of DBSCAN. As method to offset the second drawback, four types of fitness functions are defined to enable users to set the number of clusters as input. A supervised fitness function is defined to use the ESA-DCC as a classifier to address the third drawback. Four ESA- DCC methods, GA-DCC, PSO-DCC, DE-DCC and ABC-DCC, are developed. The performance of the ESA-DCC methods is compared with K-means and DBSCAN using ten datasets. The experimental results indicate that the proposed ESA-DCC methods can find the optimised parameters in both supervised and unsupervised contexts. The proposed methods are applied in a product recommender system and image segmentation cases.
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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 levures oenologiques en mettant l'accent sur des facteurs de stress caractéristiques des vins rouges et des vins blancs. La température et le stress osmotique affectent fortement cette phase pour toutes les souches, alors que le SO2, les lipides et la thiamine ont un effet souche-dépendant. Ces données ont servi de base à deux approches parallèles. Une approche d'évolution expérimentale a permis, en appliquant des pressions sélectives caractéristiques de la phase de latence, de sélectionner des souches évoluées présentant une phase de latence plus courte. Plusieurs mutations de novo potentiellement impliquées dans le phénotype évolué ont été identifiées par séquençage de leur génome. En parallèle, une approche QTL combinant des croisements inter-souches, une étape de propagation industrielle et séchage des descendants, et la sélection de cellules bourgeonnantes par FACS a été développée. Ces deux stratégies ont permis d’identifier plusieurs variants alléliques impliqués dans la paroi cellulaire, le transport du glucose, le cycle cellulaire et la résistance au stress, jouant un rôle potentiellement important pendant la phase de latence. L’ensemble de ces résultats apporte de nouvelles connaissances sur la diversité et les bases génétiques de l'adaptation des levures à la phase de latence oenologique et offre un cadre d’amélioration des propriétés des souches. De plus, nous avons montré que K. marxianus a un potentiel pour des cultures mixtes et des contributions aromatiques positives en conditions oenologiques, ouvrant de nouvelles possibilités pour des études ultérieures.Titre : Résistance au stress lors de la phase de latence en fermentation oenologique et développement de levures optimiséesMots clés : Fermentation oenologique, levure, phase de latence, résistance multi-stress, QTL, évolution adaptative, K. marxianus
Abstract: Saccharomyces cerevisiae has been used for millennia to perform wine fermentation due to its endurance and unmatched qualities and is nowadays widely used as wine yeast starter. Nevertheless, at the moment of inoculation, wine yeasts must cope with specific stress factors that can compromise the fermentation start. The objective of this work was to elucidate the metabolic and molecular bases of multi-stress resistance during wine fermentation lag phase. We first characterized a set of commercialized wine yeast strains by focusing on stress factors typically found at this stage in red wines and in white wines. Temperature and osmotic stress had a drastic impact in lag phase for all strains whereas SO2, low lipids and thiamine had a more strain dependent effect. Based on these data, we developed two parallel approaches. Using an evolutionary engineering approach where selective pressures typically present in lag phase were applied, we obtained evolved strains with a shorter lag phase in winemaking conditions. Whole genome sequencing allowed to identify several de novo mutations potentially involved in the evolved phenotype. In parallel, a QTL mapping approach was conducted, combining an intercross strategy, industrial propagation and drying of the progeny populations and selection of the first budding cells by FACS. Both strategies allowed the identification of several allelic variants involved in cell wall, glucose transport, cell cycle and stress resistance, as important in lag phase phenotype. Overall, these results provide a deeper knowledge of the diversity and the genetic bases of yeast adaptation to wine fermentation lag phase and a framework for improving yeast lag phase. Additionally, we showed that K. marxianus has potential for mixed cultures and positive aromatic contributions under oenological conditions, opening new possibilities for further studies.Title: Stress resistance during the lag phase of wine fermentation and development of optimized yeastsKeywords: Wine fermentation, yeast, lag phase, multi-stress resistance, QTL, adaptive evolution, K. marxianus
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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
高苑科技大學
資訊科技應用研究所
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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 discuss in this marketing planning. various grades of store activities to be presented in the mobile advertising, product prices and stores follow the unspoken rules.Mobile phone mobile ad promotional messages and layout for the order on the degree of importance, such as consumers are most directly feel the price, followed by the shelf life, you must meet the first two commodities message put into the forefront of the forum to help consumersFor the latest news Paper, will be an interactive genetic algorithm optimized for marketing planning.Interactive Genetic Algorithms (Interactive Genetic Algorithm IGA) is to solve the deeper problem of subjective consciousness of the future development, IGA main concept is the basis of GA, it is desirable to replace the subjective judgment of the people, GA the direction of the fitness function, which is to determine by the human individual evolution Interactive quiz by interactive algorithms and planning staff, multimedia advertising message presentation order to optimize the work will also explore multimedia advertising messages kinds of parameters marketing planning focus on the direction and the resulting optimal solution reverseobtained results can be used to automatically configure new marketing activities, the order of presentation of multimedia advertising message
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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 designed to avoid war. Culture, particularly agriculture, changed much of that and war became common. Changing this is not to go against our nature. Naïve optimism is no more in place. There is hope of more constructive engagement between Christians and Darwinians. On the Christian side, there are alternative theologies to Augustinian Atonement theology, notable Incarnational theology, not dependent on a literal Adam and Eve. On the Darwinian side, there are fresh empirical findings and interpretations, with truer understandings of human history and nature. Perhaps now, together, we can move forward the debate on the nature and causes and possible ending of human warfare.
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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 of literature on judicial decision-making and judicial attitudes casts doubt on the ability as well as the motivations of courts to bring about efficient precedent. Demand-side complications include interest group effects, plaintiff selection effects, information selection effects, settlement selection effects, and procedural factors.
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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 does suggest that moral knowledge and virtue don’t come easily, as we are susceptible to some unsavory influences that lead to rationalizing bad behavior. Reason can be corrupted in ethics just as in other domains, but the science warrants cautious optimism, not a special skepticism about morality in particular. Rationality in ethics is possible not just despite, but in virtue of, the psychological and evolutionary mechanisms that shape moral cognition.
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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 of constructive, generative processes, however, can a behavior-oriented “understanding” of the environment develop. These “understanding”-oriented structures also make our mind language ready. Finally, in order to comprehend how the human mind actually comes into being, based on the considered principles, it is necessary to pursue functional, computational, and where possible, algorithmic and hardware perspectives in the sense of David Marr’s three levels of understanding.
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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 years or so, introductory books with a functional and computational perspective on how human thought and language capabilities may actually have come about – and are coming about over and over again – are missing. This book fills that gap. Starting with a historical background on traditional cognitive science and resulting fundamental challenges that have not been resolved, embodied cognitive science is introduced and its implications for how human minds have come and continue to come into being are detailed. In particular, the book shows that evolution has produced biological bodies that provide “morphologically intelligent” structures, which foster the development of suitable behavioral and cognitive capabilities. While these capabilities can be modified and optimized given positive and negative reward as feedback, to reach abstract cognitive capabilities, evolution has furthermore produced particular anticipatory control-oriented mechanisms, which cause the development of particular types of predictive encodings, modularizations, and abstractions. Coupled with an embodied motivational system, versatile, goal-directed, self-motivated behavior, learning becomes possible. These lines of thought are introduced and detailed from interdisciplinary, evolutionary, ontogenetic, reinforcement learning, and anticipatory predictive encoding perspectives in the first part of the book. A short excursus then provides an introduction to neuroscience, including general knowledge about brain anatomy, and basic neural and brain functionality, as well as the main research methodologies. With reference to this knowledge, the subsequent chapters then focus on how the human brain manages to develop abstract thought and language. Sensory systems, motor systems, and their predictive, control-oriented interactions are detailed from a functional and computational perspective. Bayesian information processing is introduced along these lines as are generative models. Moreover, it is shown how particular modularizations can develop. When control and attention come into play, these structures develop also dependent on the available motor capabilities. Vice versa, the development of more versatile motor capabilities depends on structural development. Event-oriented abstractions enable conceptualizations and behavioral compositions, paving the path towards abstract thought and language. Also evolutionary drives towards social interactions play a crucial role. Based on the developing sensorimotor- and socially-grounded structures, the human mind becomes language ready. The development of language in each human child then further facilitates the self-motivated generation of abstract, compositional, highly flexible thought about the present, past, and future, as well as about others. In conclusion, the book gives an overview over how the human mind comes into being – sketching out a developmental pathway towards the mastery of abstract and reflective thought, while detailing the critical body and neural functionalities, and computational mechanisms, which enable this development.
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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, 165–90. Boca Raton: 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, 887–95. Berlin, Heidelberg: 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, 30–45. Cham: 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, 9–15. Boston, MA: 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, 89–120. Cham: 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, 625–35. Cham: 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, 143–64. Berlin, Heidelberg: 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, 141–50. Berlin, Heidelberg: 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, 213–25. Berlin, Heidelberg: 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, 335–46. Cham: 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. New York, New York, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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 target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a Hybrid-Game strategy to a MOEA and for a MPPS.
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Reports on the topic "Evolutionary Optimiser"

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin, and Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, December 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 the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.
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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, September 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 against bovine babesiosis, and for additional research to better characterize rhoptry associated protein-1 (RAP-1) as a target of protective immunity. In this continuation BARD project, our objectives were to [1] optimize the immune response against RAP-1, and [2] identify additional rhoptry candidate vaccine antigens. The entire locus encoding B. bovis RAP-1 was sequenced, and the rap-1 open reading frame compared among several strains. Unlike B. bigemina, in which multiple gene copies with variant domains encode RAP-1, the B. bovis RAP-1 locus contains only two identical genes which are conserved among strains. Through testing of multiple truncated constructs of rRAP-1, one or more immunodominant T cell epitopes were mapped to the amino terminal half of RAP-1. At least one linear and one conformational B cell epitope have been demonstrated in the same amino terminal construct, which in B. bigemina RAP-1 also contains an epitope recognized by neutralizing antibody. The amine terminal half of the molecule represents the most highly conserved part of the gene family and contains motifs conserved broadly among the apicomplexa. In contrast, the carboxy terminal half of B. bovis RAP-1 is less well conserved and contains multiple repeats encoding a linear B cell epitope potentially capable of inducing an ineffective, T cell independent, type 2 immune response. Therefore, we are testing an amino terminal fragment of RAP-1 (RAP-1N) in an immunization trial in cattle. Cattle have beer immunized with RAP-1N or control antigen, and IL-12 with Ribi adjuvant. Evaluation of the immune response is ongoing, and challenge with virulent B. bovis will occur in the near future. While no new rhoptry antigens were identified, our studies did identify and characterize a new spherical body antigen (SBP3), and several heat shock proteins (HSP's). The SBP3 and HSP21 antigens stimulate T cells from immune cattle and are considered new vaccine candidates worthy of further testing. Overall, we conclude that a single RAP-1 vaccine construct representing the conserved amino terminal region of the molecule should be sufficient for immunization against all strains of B. bovis. While results of the ongoing immunization trial will direct our next research steps, results at this time are consistent with our long term goal of designing a subunit vaccine which contains only the epitopes relevant to induction of protective immunity. Parallel studies are defining the mechanisms of protective immunity. Apicomplexan protozoa, including babesiosis and malaria, cause persistent diseases for which control is inadequate. The apical organelles are defining features of these complex protozoa, and have been conserved through the evolutionary process, Past and current BARD projects on babesiosis have established the validity and potential of exploiting these conserved organelles in developing improved control methods applicable to all apicomplexan diseases.
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