evolutionary algorithms (EAs) have been recognized to be well suited to approximate the Pareto front of Multi-objective Optimization Problems (MOPs). In reality, the Decision Maker (DM) is not interested in discoverin...
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evolutionary algorithms (EAs) have been recognized to be well suited to approximate the Pareto front of Multi-objective Optimization Problems (MOPs). In reality, the Decision Maker (DM) is not interested in discovering the whole Pareto front rather than finding only the portion(s) of the front that matches at most his/her preferences. Recently, several studies have addressed the decision-making task to assist the DM in choosing the final alternative. Knee regions are potential parts of the Pareto front presenting the maximal trade-offs between objectives. Solutions residing in knee regions are characterized by the fact that a small improvement in either objective will cause a large deterioration in at least another one which makes moving in either direction not attractive. Thus, in the absence of explicit DM's preferences, we suppose that knee regions represent the DM's preferences themselves. Recently, few works were proposed to find knee regions. This paper represents a further study in this direction. Hence, we propose a new evolutionary method, denoted TKR-NSGA-II, to discover knee regions of the Pareto front. In this method, the population is guided gradually by means of a set of mobile reference points. Since the reference points are updated based on trade-off information, the population converges towards knee region centers which allows the construction of a neighborhood of solutions in each knee. The performance assessment of the proposed algorithm is done on two- and three-objective knee-based test problems. The obtained results show the ability of the algorithm to: (1) find the Pareto optimal knee regions, (2) control the extent (We mean by extent the breadth/spread of the obtained knee region.) of the obtained regions independently of the geometry of the front and (3) provide competitive and better results when compared to other recently proposed methods. Moreover, we propose an interactive version of TKR-NSGA-II which is useful when the DM has no a prior
A non-Darwinian evolutionary algorithm is presented as search engine to identify the characteristics of a source of atmospheric pollutants, given a set of concentration measurements. The algorithm drives iteratively a...
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A non-Darwinian evolutionary algorithm is presented as search engine to identify the characteristics of a source of atmospheric pollutants, given a set of concentration measurements. The algorithm drives iteratively a forward dispersion model from tentative sources toward the real source. The solutions of non-Darwinian evolution processes are not generated through pseudo-random operators, unlike traditional evolutionary algorithms, but through a reasoning process based on machine learning rule generation and instantiation. The new algorithm is tested with both a synthetic case and with the Prairie Grass field experiment. To further test the capabilities of the algorithm to work in real-world scenarios, the source identification of all Prairie Grass releases was performed with a decreasing number of sensor measurements, and a relationship is found between the precision of the solution, the number of sensors available, and the levels of concentration measured by the sensors. The proposed methodology can be used for a variety of optimization problems, and is particularly suited for problems where the operations needed for evaluating new candidate solutions are computationally expensive. Published by Elsevier Ltd.
The proceedings contain 18 papers. The topics discussed includes: a hybrid random subspace classifier fusion approach for protein mass spectra classification;improving the performance of hierarchical classification wi...
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ISBN:
(纸本)3540787569
The proceedings contain 18 papers. The topics discussed includes: a hybrid random subspace classifier fusion approach for protein mass spectra classification;improving the performance of hierarchical classification with swarm intelligence;protein interaction inference using article swarm optimization algorithm;detection of quantitative trait associated genes using cluster analysis;frequent subsplit representation of leaf-labeled trees;inference on missing values in genetic networks using high-throughput data;mining gene expression patterns for the discovery of overlapping clusters;gene selection and cancer microarray data classification via mixed-integer optimization;enhancing parameter estimation of biochemical networks by exponentially scaled search steps;a wrapper-based feature selection method for ADMET prediction using evolutionary computing;and detection of protein complexes in protein interaction networks using n-clubs.
In this paper, we proposed a new method to solve TSP (Traveling Salesman Problem) based on evolutionary algorithms. This method can be used for related problems and we found out the new method can works properly in pr...
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ISBN:
(纸本)9780387741604
In this paper, we proposed a new method to solve TSP (Traveling Salesman Problem) based on evolutionary algorithms. This method can be used for related problems and we found out the new method can works properly in problems based on permutation. We compare our results by the previous algorithms and show that our algorithm needs less time in comparison with known algorithms and so efficient for such problems.
Dynamic linear functions on the boolean hypercube are functions which assign to each bit a positive weight, but the weights change over time. Throughout optimization, these functions maintain the same global optimum, ...
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Dynamic linear functions on the boolean hypercube are functions which assign to each bit a positive weight, but the weights change over time. Throughout optimization, these functions maintain the same global optimum, and never have defecting local optima. Nevertheless, it was recently shown [Lengler, Schaller, FOCI 2019] that the (1 + 1)-evolutionary Algorithm needs exponential time to find or approximate the optimum for some algorithm configurations. In this experimental paper, we study the effect of larger population sizes for dynamic binval, the extreme form of dynamic linear functions. We find that moderately increased population sizes extend the range of efficient algorithm configurations, and that crossover boosts this positive effect substantially. Remarkably, similar to the static setting of monotone functions in [Lengler, Zou, FOGA 2019], the hardest region of optimization for (mu + 1)-EA is not close the optimum, but far away from it. In contrast, for the (mu + 1)-GA, the region around the optimum is the hardest region in all studied *** check and confirm the inserted city name is correctly ***.
One of the major difficulties for geotechnical engineers during project phase is to estimate the geomechanical parameters values of the adopted constitutive model in a reliable way. In project phase, they are normally...
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One of the major difficulties for geotechnical engineers during project phase is to estimate the geomechanical parameters values of the adopted constitutive model in a reliable way. In project phase, they are normally evaluated by laboratory and in situ tests and, in the specific case of rock masses, by the application of empirical classification systems. However, all methodologies lead to uncertainties due to factors like local heterogeneities, representativeness of the tests, etc. In order to reduce these uncertainties, geotechnical engineers can use inverse analysis during construction, using monitoring data to identify the parameters of the involved formations. This paper shows the back analysis of geomechanical parameters by the optimisation of a 3D numerical model of the hydroelectric powerhouse cavern of Venda Nova II built in Portugal. For this purpose, two optimisation techniques were considered: one classical optimisation algorithm and an evolutionary optimisation algorithm. In the optimisation process, displacements measured by extensometers during excavation were used to identify rock mass parameters, namely the deformability modulus (E) and the stress ratio (K-0). Efficiency of both algorithms is evaluated and compared. Both approaches allowed obtaining the optimal set of parameters and provided a better insight about the involved rock formation properties. (C) 2011 Elsevier Ltd. All rights reserved.
We present an evolutionary Visual Exploration (EVE) system that combines visual analytics with stochastic optimisation to aid the exploration of multidimensional datasets characterised by a large number of possible vi...
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DAEX is a metaheuristic designed to improve the plan quality and the scalability of an encapsulated planning system. DAEX is based on a state decomposition strategy, driven by an evolutionary algorithm, which benefits...
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Recently, the automation of picking work has advanced in the factory for the reduction of labor costs. When picking work is automatic, it is very important to estimate posture of target components. Therefore, an algor...
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Data-driven models were constructed for the Productivity, CO2 emission, and Si content for an operational Blast furnace using evolutionary approaches that involved two recent strategies based upon bi-objective genetic...
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Data-driven models were constructed for the Productivity, CO2 emission, and Si content for an operational Blast furnace using evolutionary approaches that involved two recent strategies based upon bi-objective genetic Programming and neural nets evolving through Genetic algorithms. The models were utilized to compute the optimum tradeoff between the level of CO2 emission and productivity at different Si levels, using a Predator-Prey Genetic Algorithm, well tested for computing the Pareto-optimality. The results were pitted against some similar calculations performed with commercial softwares and also compared with the results of thermodynamics-based analytical models.
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