The profitability of the livestock industry largely depends on cost-effective feed ration formulation as feed accounts for between 60 and 80% of production costs. Therefore, feed formulation is a recurring problem for...
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The profitability of the livestock industry largely depends on cost-effective feed ration formulation as feed accounts for between 60 and 80% of production costs. Therefore, feed formulation is a recurring problem for breeders. In addition, the presence of linear and non-linear constraints, and multiple possible combinations that are subject to upsurge makes the formulation of feed a Non-deterministic Polynomial-time hard (NP-hard) problem. Generally, feed formulation is done by specifying the nutritional requirements as rigid constraints and an algorithm attempts to find a feasible cost-effective formulation. However, relaxing the constraints can sometimes provide a huge reduction in the cost of feed while not seriously affecting the economic performance of the livestock. This entails the development of a feed formulation software that has an inbuilt mechanism to enable relaxation to the constraints based on the users' necessities. Accordingly, the problem formulation and the optimization algorithm should facilitate this. We modified the conventional problem formulation with a tolerance parameter (as a percentage of the actual value) to accommodate the relaxation of constraints. We solved this problem with differential evolution, a variant of evolutionary algorithms, which are good for handling NP-hard problems. In addition, the relaxation of the constraints was done in an interactive way using the proposed method without penalties. In other words, the proposed method is flexible and possesses the ability to search for a feasible and least-cost solution if available or otherwise, the best solution and finds the suitable feed components to be used in ration formulation at an optimal cost depending on the nutrient requirements and growth stage of the animal.
Context: evolutionary algorithms typically require large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate. Objective: To solve search-based software engineering (SE) ...
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Context: evolutionary algorithms typically require large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate. Objective: To solve search-based software engineering (SE) problems, using fewer evaluations than evolutionary methods. Method: Instead of mutating a small population, we build a very large initial population which is then culled using a recursive bi-clustering chop approach. We evaluate this approach on multiple SE models, unconstrained as well as constrained, and compare its performance with standard evolutionary algorithms. Results: Using just a few evaluations (under 100), we can obtain comparable results to state-of-the-art evolutionary algorithms. Conclusion: Just because something works, and is widespread use, does not necessarily mean that there is no value in seeking methods to improve that method. Before undertaking search-based SE optimization tasks using traditional EAs, it is recommended to try other techniques, like those explored here, to obtain the same results with fewer evaluations. (C) 2017 Elsevier B.V. All rights reserved.
evolutionary optimization algorithms by imitating survival of the best features and transmutation of the creatures within their generation, approach complicated engineering problems very well. Similar to many other fi...
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evolutionary optimization algorithms by imitating survival of the best features and transmutation of the creatures within their generation, approach complicated engineering problems very well. Similar to many other field of research, civil engineering problems have benefited from this capacity. In the current study, optimum design of retaining walls under seismic loading case is analyzed by three evolutionary algorithms, differential evolution (DE), evolutionary strategy (ES), and biogeography-based optimization algorithms (BBO). All the results are benchmarked with the classical evolutionary algorithm, genetic algorithm (GA). To this end, two different measures, minimum-cost and minimum-weight, are considered based on ACI 318-05 requirements coupled with geotechnical considerations for retaining walls. Numerical simulations on three case studies revealed that BBO reached the best results over all the case studies decisively.
Context evolutionary algorithms have been shown to be effective at generating unit test suites optimised for code coverage. While many specific aspects of these algorithms have been evaluated in detail (e.g., test len...
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Context evolutionary algorithms have been shown to be effective at generating unit test suites optimised for code coverage. While many specific aspects of these algorithms have been evaluated in detail (e.g., test length and different kinds of techniques aimed at improving performance, like seeding), the influence of the choice of evolutionary algorithm has to date seen less attention in the literature. Objective: Since it is theoretically impossible to design an algorithm that is the best on all possible problems, a common approach in software engineering problems is to first try the most common algorithm, a genetic algorithm, and only afterwards try to refine it or compare it with other algorithms to see if any of them is more suited for the addressed problem. The objective of this paper is to perform this analysis, in order to shed light on the influence of the search algorithm applied for unit test generation. Method: We empirically evaluate thirteen different evolutionary algorithms and two random approaches on a selection of non-trivial open source classes. All algorithms are implemented in the Evosuite test generation tool, which includes recent optimisations such as the use of an archive during the search and optimisation for multiple coverage criteria. Results: Our study shows that the use of a test archive makes evolutionary algorithms clearly better than random testing, and it confirms that the DynaMOSA many-objective search algorithm is the most effective algorithm for unit test generation. Conclusion: Our results show that the choice of algorithm can have a substantial influence on the performance of whole test suite optimisation. Although we can make a recommendation on which algorithm to use in practice, no algorithm is clearly superior in all cases, suggesting future work on improved search algorithms for unit test generation.
Increased nutrient loads and changed nutrient ratios in estuarine waters have enhanced the occurrence of eutrophication and harmful algae blooms. Most of these consequences are caused by the new proliferation of toxin...
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Increased nutrient loads and changed nutrient ratios in estuarine waters have enhanced the occurrence of eutrophication and harmful algae blooms. Most of these consequences are caused by the new proliferation of toxin-producing non-siliceous algae. In this study, we propose a multi-objective reservoir operation model based on 10-day time scale for estuarine eutrophication control to reduce the potential non-siliceous algae outbreak. This model takes the hydropower generation and social economy water requirement in reservoir into consideration, minimizing the ICEP (indicator of estuarine eutrophication potential) as an ecological objective. Three modem multi-objective evolutionary algorithms (MOEAs) are applied to solve the proposed reservoir operation model. The Three Gorges Reservoir and its operation effects on the Yangtze Estuary were chosen as a case study. The performances of these three algorithms were evaluated through a diagnostic assessment framework of modem MOEAs' abilities. The results showed that the multi-objective evolutionary algorithm based on decomposition with differential evolution operator (MOEA/D-DE) achieved the best performance for the operation model. It indicates that single implementation of hydrological management cannot make effective control of potential estuarine eutrophication, while combined in-estuary TP concentration control and reservoir optimal operation is a more realistic, crucial and effective strategy for controlling eutrophication potential of non-siliceous algae proliferation. Under optimized operation with controlled TP concentration and estuarine water withdrawal of 1470 m(3)/s, ecological satiety rate for estuarine drinking water source increased to 77.78%, 88.89% and 83.33% for wet, normal and dry years, the corresponding values in practical operation were only 72.22%, 58.33% and 55.56%, respectively. The results suggest that these operations will not negatively affect the economic and social interests. Therefore, the p
Modern cities are currently facing rapid urban growth and struggle to maintain a sustainable development. In this context, "eco-neighbourhoods" became the perfect place for testing new innovative ideas that ...
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Modern cities are currently facing rapid urban growth and struggle to maintain a sustainable development. In this context, "eco-neighbourhoods" became the perfect place for testing new innovative ideas that would reduce congestion and optimize traffic flow. The main motivation of this work is a true and stated need of the Department of Transport in Nancy, France, to improve the traffic flow in a central eco-neighbourhood currently under reconfiguration, reduce travel times and test various traffic control scenarios for a better interconnectivity between urban intersections. Therefore, this paper addresses a multi-objective simulation-based signal control problem through the case study of "Nancy Grand Coeur" (NGC) eco-neighbourhood with the purpose of finding the optimal traffic control plan to reduce congestion during peak hours. Firstly, we build the 3D mesoscopic simulation model of the most circulated intersection (C129) based on specifications from the traffic management centre. The simulation outputs from various scenario testing will be then used as inputs for the optimisation and comparative analysis modules. Secondly, we propose a multi-objective optimization method by using evolutionary algorithms and find the optimal traffic control plan to be used in C129 during morning and evening rush hours. Lastly, we take a more global view and extend the 3D simulation model to three other interconnected intersections, in order to analyse the impact of local optimisation on the surrounding traffic conditions in the eco-neighbourhood. The current proposed simulation-optimisation framework aims at supporting the traffic engineering decision-making process and the smart city dynamic by favouring a sustainable mobility.
Inherent part of evolutionary algorithms that are based on Darwin's theory of evolution and Mendel's theory of genetic heritage, are random processes since genetic algorithms and evolutionary strategies are us...
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Inherent part of evolutionary algorithms that are based on Darwin's theory of evolution and Mendel's theory of genetic heritage, are random processes since genetic algorithms and evolutionary strategies are used. In this paper, we present extended experiments (of our previous) of selected evolutionary algorithms and test functions showing whether random processes really are needed in evolutionary algorithms. In our experiments we used differential evolution and SOMA algorithms with functions 2ndDeJong, Ackley, Griewangk, Rastrigin, SineWave and StretchedSineWave. We use n periodical deterministic processes (based on deterministic chaos principles) instead of pseudo-random number generators (PRGNs) and compare performance of evolutionary algorithms powered by those processes and by PRGNs. Results presented here are numerical demonstrations rather than mathematical proofs. We propose the hypothesis that a certain class of deterministic processes can be used instead of PRGNs without lowering the performance of evolutionary algorithms.
In this work, an efficient design of multiplier-less digital finite impulse response (FIR) filter is presented, where the sub-expression elimination (SE) algorithms are employed on filter coefficients, and optimizatio...
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In this work, an efficient design of multiplier-less digital finite impulse response (FIR) filter is presented, where the sub-expression elimination (SE) algorithms are employed on filter coefficients, and optimization is done with evolutionary algorithms. This FIR filter is designed with novelty of optimizing the quantized coefficients inside each of the respective optimization algorithm, instead of using two separate algorithms: one for generation of optimal continuous coefficients, and second for optimizing the quantized coefficients. Comparative analysis using different SE techniques have been utilized for reducing the requirement of adders on both binary represented and canonic signed digit converted filter coefficients. The simulation results illustrate the impact of proposed algorithm along with significant reduction in number of adders.
In pattern recognition, the classification accuracy has a strong correlation with the selected features. Therefore, in the present paper, we applied an evolutionary algorithm in combination with linear discriminant an...
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In pattern recognition, the classification accuracy has a strong correlation with the selected features. Therefore, in the present paper, we applied an evolutionary algorithm in combination with linear discriminant analysis (LDA) to enhance the feature selection in a static image-based facial expressions system. The accuracy of the classification depends on whether the features are well representing the expression or not. Therefore the optimization of the selected features will automatically improve the classification accuracy. The proposed method not only improves the classification but also reduces the dimensionality of features. Our approach outperforms linear-based dimensionality reduction algorithms and other existing genetic-based feature selection algorithms. Further, we compare our approach with VGG (Visual Geometry Group)-face convolutional neural network (CNN), according to the experimental results, the overall accuracy is 98.67% for either our approach or VGG-face. However, the proposed method outperforms CNN in terms of training time and features size. The proposed method proves that it is able to achieve high accuracy by using far fewer features than CNN and within a reasonable training time.
This study presents a methodology which integrates single-objective evolutionary algorithms (EAs) and finite element (FE) model updating for damage inference in three-dimensional (3D) structures. First, original well-...
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This study presents a methodology which integrates single-objective evolutionary algorithms (EAs) and finite element (FE) model updating for damage inference in three-dimensional (3D) structures. First, original well-known EAs, namely the genetic algorithm, differential evolution (DE) and particle swarm optimization (PSO), are combined with FE model updating for detecting damage in a 3D four-storey modular structure and their performances are compared. Next, to obtain more accurate results, hybrid Levy flights-DE and hybrid artificial bee colony-PSO are developed for enhancing damage identification. With each method, the objective function composed of modal strain energy and mode shape residuals, taken from the FE model of the intact structure and the simulated damage responses, is initially created. Then, the performance of each algorithm combined with FE model updating for damage detection is assessed in terms of three characteristics: consistency, computational cost and accuracy, and the best performing algorithm is recommended.
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