The paper concerns the use of evolutionary algorithms to solve the problem of multiobjective optimization and learning of fuzzy cognitive maps (FCMs) on the basis of multidimensional medical data related to diabetes. ...
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ISBN:
(纸本)9783030345006;9783030344993
The paper concerns the use of evolutionary algorithms to solve the problem of multiobjective optimization and learning of fuzzy cognitive maps (FCMs) on the basis of multidimensional medical data related to diabetes. The aim of this research study is an automatic construction of a collection of FCM models based on various criteria depending on the structure of the model and forecasting capabilities. The simulation analysis was performed with the use of the developed multiobjective Individually Directional evolutionary Algorithm. Experiments show that the collection of fuzzy cognitive maps, in which each element is built on the basis of particular patient data, allows us to receive higher forecasting accuracy compared to the standard approach. Moreover, by appropriate aggregation of these collections we can also obtain satisfactory accuracy of forecasts for the new patient.
With maritime industry carrying out nearly 90% of the volume of global trade, the algorithms and solutions to provide Quality of Services (QOS) [18] in maritime transportation are of great importance to both academia ...
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With maritime industry carrying out nearly 90% of the volume of global trade, the algorithms and solutions to provide Quality of Services (QOS) [18] in maritime transportation are of great importance to both academia and the industry. This research investigates an optimization problem us- ing evolutionary algorithms and big data analytics to address an important challenge in maritime disruption management, and illustrates how it can be engaged with information technologies and Internet of Things (IoT). Ac- cordingly, in this thesis, we design, develop and evaluate methods to improve decision support systems (DSSs) in maritime supply chain management. We pursue three research goals in this thesis. First, the Vessel Sched- ule Recovery Problem (VSRP) is reformulated and a bi-objective optimiza- tion approach is proposed. We employ bi-objective evolutionary algorithms (MOEAs) to solve optimization problems. An optimal Pareto front provides a valuable trade-off between two objectives (minimizing delay and minimiz- ing financial loss) for a stakeholder in the freight ship company. We evaluate the problem in three domains, namely scalability analysis, vessel steaming policies, and voyage distance analysis, and statistically validate their perfor- mance significance. According to the experiments, the problem complexity varies in different scenarios, while Non-dominated Sorting Genetic Algorithm II (NSGAII) performs better than other MOEAs in all scenarios. In the second work, a new data-driven VSRP is proposed, which benefits from the available Automatic Identification System (AIS) data [2]. In the new formulation, the trajectory between the port calls is divided and encoded into adjacent geohashed [133] regions. In each geohash, the historical speed profiles are extracted from AIS data. This results in a large-scale optimiza- tion problem called Granular Speed-based Vessel Schedule Recovery Problem (G-S-VSRP) with three objectives (i. e., minimizing loss, delay, and maxim
Known as two cornerstones of problem solving by search, exploitation and exploration are extensively discussed for implementation and application of evolutionary algorithms (EAs). However, only a few researches focus ...
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Deep learning is a very popular gradient based search technique nowadays. In this field of machine learning we usually apply neural networks with various structure. The algorithms of the deep learning techniques and t...
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We study the task of replicating the functionality of black-box neural models, for which we only know the output class probabilities provided for a set of input images. We assume back-propagation through the black-box...
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This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length o...
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This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length of reinforcements and vertical distance of reinforced layers. Design restrictions control the final design to be valid in terms of constraints. The aim is to explore the efficiency of evolutionary-based algorithms in dealing with MSE optimization problem along with automating the minimum cost design of MSE walls. To this end, three evolutionary algorithms, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm (BBO), are tackled to solve this problem. Comprehensive computational simulations confirm the impact of different effective parameters variation on the final design. Finally, the BBO algorithm performed the best, while DE recorded the most unsatisfactory results.
Multiobjective evolutionary algorithms are successfully applied in many real-world multiobjective optimization problems. As for many other AI methods, the theoretical understanding of these algorithms is lagging far b...
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Multiobjective evolutionary algorithms are successfully applied in many real-world multiobjective optimization problems. As for many other AI methods, the theoretical understanding of these algorithms is lagging far behind their success in practice. In particular, previous theory work considers mostly easy problems that are composed of unimodal objectives. As a first step towards a deeper understanding of how evolutionary algorithms solve multimodal multiobjective problems, we propose the OneJumpZeroJump problem, a bi-objective problem composed of two objectives isomorphic to the classic jump function benchmark. We prove that the simple evolutionary multiobjective optimizer (SEMO) with probability one does not compute the full Pareto front, regardless of the runtime. In contrast, for all problem sizes n and all jump sizes (formula presented), the global SEMO (GSEMO) covers the Pareto front in an expected number of Θ((n − 2k)nk) iterations. For k = o(n), we also show the tighter bound (formula presented), which might be the first runtime bound for an MOEA that is tight apart from lower-order terms. We also combine the GSEMO with two approaches that showed advantages in single-objective multimodal problems. When using the GSEMO with a heavy-tailed mutation operator, the expected runtime improves by a factor of at least kΩ(k). When adapting the recent stagnation-detection strategy of Rajabi and Witt [RW22] to the GSEMO, the expected runtime also improves by a factor of at least kΩ(k) and surpasses the heavy-tailed GSEMO by a small polynomial factor in k. Via an experimental analysis, we show that these asymptotic differences are visible already for small problem sizes: A factor-5 speed-up from heavy-tailed mutation and a factor-10 speed-up from stagnation detection can be observed already for jump size 4 and problem sizes between 10 and 50. Overall, our results show that the ideas recently developed to aid single-objective evolutionary algorithms to cope with local opt
In the field of game designing, artificial intelligence is used to generate responsive, adaptive, or intelligent behaviors primarily in Non-Player-Characters(NPCs). There is a large demand for controlling game AI sinc...
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In the field of game designing, artificial intelligence is used to generate responsive, adaptive, or intelligent behaviors primarily in Non-Player-Characters(NPCs). There is a large demand for controlling game AI since a variety of players expect to be provided NPC opponents with appropriate difficulties to improve their game experience. However, to the best of our knowledge, a few works are focusing on this problem. In this paper, we firstly present a Reinforced evolutionary Algorithm based on the Difficulty-Difference objective(REA-DD) to the DLAI problem, which combines reinforcement learning and evolutionary algorithms. REA-DD is able to generate the desired difficulty level of game AI accurately. Nonetheless, REA can only obtain a kind of game AI in each run. To improve efficiency, another algorithm based on Multi-objective Optimization is proposed, regarded as RMOEA-DD, which obtains DLAI after one run. Experiments on the game Pong from ALE and apply on a commercial game named The Ghost Story to show that our algorithms provide valid methods to the DLAI problem both in the term of controlling accuracy and efficiency.
Game-playing evolutionary algorithms, specifically Rolling Horizon evolutionary algorithms, have recently managed to beat the state of the art in performance across many games. However, the best results per game are h...
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