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In this paper, a novel procedure for optimal design of geometrically nonlinear submerged arches is proposed. It is based on the Coral Reefs Optimization with Substrate Layers algorithm, a multi-method ensemble evolutionary approach for solving optimization problems. A novel arch shape parameterization is combined with the Coral Reefs Optimization with Substrate Layers algorithm. This new parameterization allows considering geometrical parameters in the design process, in addition to the reduction of the bending moment carried out by the classical design approach. The importance of considering the second-order behaviour of the arch structure is shown by different numerical experiments. Moreover, it is shown that the use of Coral Reefs Optimization with Substrate Layers algorithm leads to nearly-optimal solutions, ensuring the stability of the structure, reducing the maximum absolute bending moment value, and complying with the serviceability structural restrictions. [ABSTRACT FROM AUTHOR]

Decision Trees (DTs) are a class of supervised learning models that are widely used for both classification and regression applications. They are well-known for their interpretability and robustness, which have led them to remain popular even 60 years after they were first proposed. However, because traditional tree algorithms use greedy methods that are prone to suboptimality, several works have explored the usage of evolutionary algorithms instead. Although these algorithms are often reported to outperform the traditional greedy approach, their computational cost is much higher, since the evolutionary component requires a large number (millions or billions) of function evaluations in order to produce a single tree. Aiming to reduce this computational cost, in this work we propose an encoding that allows the training and evaluation of DTs using only matrix operations. The proposed procedure is shown to be much faster than the traditional tree implementation for complete trees with depths ranging from 2 to 6, and for datasets ranging in size from 100 to 100,000 observations. In particular, the results show speedups of nearly up to 20 times, especially when the dataset is large and the desired tree is small enough to be interpretable. The proposed procedure also benefits from GPU parallelization, although it is still highly performing without it. Furthermore, we propose an evolutionary algorithm, called Coral Reef Optimization for Decision Trees (CRO-DT), that integrates this encoding with a pre-existing ensemble algorithm to evolve better univariate trees. The results obtained show that the proposed CRO-DT is competitive with traditional and modern tree algorithms, consistently producing models of good quality across 14 tested UCI Datasets. We conclude that for most relevant situations, the proposed matrix encoding provides significant speedups over the traditional implementation, and also may serve as a basis for high quality evolutionary DT algorithms. • We propose a new matrix encoding to efficiently evolve Decision Trees. • This encoding accelerates prediction through efficient numerical computations. • Great speedups are achieved for large datasets and small trees. • We introduce a tree algorithm with this encoding that uses Coral Reef Optimization. • The resulting approach searches wider and is competitive with other tree algorithms. [ABSTRACT FROM AUTHOR]

The prediction of convective clouds formation is a very important problem in different areas such as agriculture, natural hazards prevention or transport-related facilities. In this paper, we evaluate the capacity of different types of evolutionary artificial neural networks to predict the formation of convective clouds, tackling the problem as a classification task. We use data from Madrid-Barajas airport, including variables and indices derived from the Madrid-Barajas airport radiosonde station. As objective variable, we use the cloud information contained in the METAR and SPECI meteorological reports from the same airport and we consider a prediction time horizon of 12 h. The performance of different types of evolutionary artificial neural networks has been discussed and analysed, including three types of basis functions (sigmoidal unit, product unit and radial basis function) and two types of models, a mono-objective evolutionary algorithm with two objective functions and a multi-objective evolutionary algorithm optimised by the two objective functions simultaneously. We show that some of the developed neuro-evolutionary models obtain high quality solutions to this problem, due to its high unbalance characteristic. [ABSTRACT FROM AUTHOR]

In the Era of exponential data generation, increasing the number of paleoclimate records to improve climate field reconstructions might not always be the best strategy. By using pseudo-proxies from different model ensembles, we show how biologically-inspired artificial intelligence can be coupled with different reconstruction methods to minimize the spatial bias induced by the non-homogeneous distribution of available proxies. The results indicate that small subsets of records situated over representative locations can outperform the reconstruction skill of the full proxy network, even in more realistic pseudo-proxy experiments and observational datasets. These locations highlight the importance of high-latitude regions and major teleconnection areas to reconstruct annual global temperature fields and their responses to external forcings and internal variability. However, low frequency temperature variations such as the transition between the Medieval Climate Anomaly and the Little Ice Age are better resolved by records situated at lower latitudes. According to our idealized experiments a careful selection of proxy locations should be performed depending on the targeted time scale of the reconstructed field. [ABSTRACT FROM AUTHOR]

Nowadays, one of the priorities of the European Commission is to reduce the environmental impact of aviation through the advanced design of novel aircraft configurations. This is of utmost importance in order to decrease the environmental footprint of aviation and to reduce fuel consumption and make airlines more profitable. This implies that new methods and tools for aerodynamic shape optimization will have to be developed, allowing aircraft configurations that cannot be obtained with traditional strategies. This paper focuses on the application of enhanced methods in aerodynamic shape design optimization to enable advanced aircraft configurations. In particular, this work aims to demonstrate the feasibility of the proposed strategy to reach optimal configurations that are far away from its baseline geometry. For this purpose, evolutionary algorithms are combined with support vector machines and applied to the optimization of a baseline geometry for different flow conditions. In particular, the selected application is based on the shape optimization problem of the landing gear master cylinder. Results pointed out the feasibility of the mentioned strategy to enable novel configurations within an aerodynamic shape optimization process. [ABSTRACT FROM AUTHOR]

This paper is focused on reducing the number of elements in time series with minimum information loss, with specific applications on time series segmentation. A modification of the coral reefs optimization metaheuristic (CRO) is proposed for this purpose, which is called statistical CRO (SCRO), where the main parameters of the algorithm are adjusted based on the mean and standard deviation associated with the fitness distribution. Moreover, the algorithm is combined with the Bottom-Up and Top-Down methodologies (traditional local search methods for time series segmentation), resulting in a hybrid methodology (HSCRO). We evaluate the performance of these algorithms using 16 time series from different application areas. The statistically-driven version of CRO is shown to improve the results of the standard CRO, eliminating the necessity of manually adjusting the main parameters of the algorithm and dynamically adjusting these parameters throughout the evolution. Moreover, when compared with other local search methods and metaheuristics from the state of the art, HSCRO shows robust segmentation results, consistently obtaining lower approximation errors. [ABSTRACT FROM AUTHOR]

In this work, we describe an approach that allows for optimizing the structure of a smart grid (SG) with renewable energy (RE) generation against abnormal conditions (imbalances between generation and consumption, overloads or failures arising from the inherent SG complexity) by combining the complex network (CN) and evolutionary algorithm (EA) concepts. We propose a novel objective function (to be minimized) that combines cost elements, related to the number of electric cables, and several metrics that quantify properties that are beneficial for SGs (energy exchange at the local scale and high robustness and resilience). The optimized SG structure is obtained by applying an EA in which the chromosome that encodes each potential network (or individual) is the upper triangular matrix of its adjacency matrix. This allows for fully tailoring the crossover and mutation operators. We also propose a domain-specific initial population that includes both small-world and random networks, helping the EA converge quickly. The experimental work points out that the proposed method works well and generates the optimum, synthetic, small-world structure that leads to beneficial properties such as improving both the local energy exchange and the robustness. The optimum structure fulfills a balance between moderate cost and robustness against abnormal conditions. Our approach should be considered as an analysis, planning and decision-making tool to gain insight into smart grid structures so that the low level detailed design is carried out by using electrical engineering techniques. [ABSTRACT FROM AUTHOR]

Surrogate-based optimization (SBO) has recently found widespread use in aerodynamic shape design owing to its promising potential to speed up the whole process by the use of a low-cost objective function evaluation, to reduce the required number of expensive computational fluid dynamics simulations. However, the application of these SBO methods for industrial configurations still faces several challenges. The most crucial challenge nowadays is the ‘curse of dimensionality’, the ability of surrogates to handle a high number of design parameters. This article presents an application study on how the number and location of design variables may affect the surrogate-based design process and aims to draw conclusions on their ability to provide optimal shapes in an efficient manner. To do so, an optimization framework based on the combined use of a surrogate modelling technique (support vector machines for regression), an evolutionary algorithm and a volumetric non-uniform rational B-splines parameterization are applied to the shape optimization of a two-dimensional aerofoil (RAE 2822) and a three-dimensional wing (DPW) in transonic flow conditions. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a network clustering algorithm, based on the application of genetic operators and capable of exploiting the traffic information. [•] The algorithm can be successfully applied, rather than topology-based algorithms, to uncover relationships and detect communities whenever two nodes have a relationship though not being directly connected. [•] We compare our algorithm to other clustering algorithms, both evolutionary and non evolutionary. [•] Our algorithm outperforms all the other algorithms on all the real world datasets and outperforms all in the case of synthetic traffic matrices, excepting Newman's for the single case of a network of 75 nodes. [ABSTRACT FROM AUTHOR]

Abstract: Hyper-parameters estimation in regression Support Vector Machines (SVMr) is one of the main problems in the application of this type of algorithms to learning problems. This is a hot topic in which very recent approaches have shown very good results in different applications in fields such as bio-medicine, manufacturing, control, etc. Different evolutionary approaches have been tested to be hybridized with SVMr, though the most used are evolutionary approaches for continuous problems, such as evolutionary strategies or particle swarm optimization algorithms. In this paper we discuss the application of two different evolutionary computation techniques to tackle the hyper-parameters estimation problem in SVMrs. Specifically we test an Evolutionary Programming algorithm (EP) and a Particle Swarm Optimization approach (PSO). We focus the paper on the discussion of the application of the complete evolutionary-SVMr algorithm to a real problem of wind speed prediction in wind turbines of a Spanish wind farm. [ABSTRACT FROM AUTHOR]

Color reduction in images is an important problem in image processing, since it is a pre-processing step in applications such as image segmentation or compression. Different methods have been proposed in the literature, several of them involving nature-inspired algorithms such as neural networks. However, not many works involving evolutionary computation techniques have been applied to this problem. This paper proposes a novel evolutionary algorithm to tackle the color reduction of RGB images. The proposed evolutionary algorithm incorporates a procedure called incremental-encoding, consisting in starting the image quantization with a small number of colors, and including additional colors in a gradual form, until reaching the final number of quantization colors. In the experiments carried out we show that the incremental-encoding evolutionary algorithm improves the performance of the standard evolutionary algorithm in this problem. Also we show that it obtains better results than several existing color reduction techniques for color quantization problems. [ABSTRACT FROM AUTHOR]

A call center is a system used by companies to provide a number of services to customers, which may vary from providing simple information to gathering and dealing with complaints or more complex transactions. The design of this kind of system is an important task, since the trend is that companies and institutions choose call centers as the primary option for customer relationship management. This paper presents an evolutionary algorithm based on Dandelion encoding to obtain near-optimal service trees which represent the structure of the desired call center. We introduce several modifications to the original Dandelion encoding in order to adapt it to the specific problem of service tree design. Two search space size reduction procedures improve the performance of the algorithm. Systematic experiments have been tackled in order to show the performance of our approach: first, we tackle different synthetic instances, where we discuss and analyze several aspects of the proposed evolutionary algorithm, and second, we tackle a real application, the design of the call center of an Italian telecommunications company. In all the experiments carried out we compare our approach with a lower bound for the problem based on information theory, and also with the results of a Huffman algorithm we have used for reference. [ABSTRACT FROM AUTHOR]

In this paper a novel scheme to solve multiclass classification problems using pure evolutionary techniques is presented. Our proposal consists of the evolution of several geometric structures such as hypercubes, hyperspheres or hyperoctahedrons, to obtain a first division of the space, in which the training samples are assigned to one or several structures. In in a second step, the samples belonging to two or more structures are re-evolved in order to obtain a single classifier. We call this approach the Evolution of Geometric Structures (EGS) algorithm. Its main characteristics are described in the paper. An application of the EGS to a well known multiclass classification problem, the automatic classification of high range resolution (HRR) radar targets, is also discussed. [ABSTRACT FROM AUTHOR]

Friction-induced stick-slip vibrations are one of the major causes for down-hole drill-string failures. Consequently, several nonlinear models and control approaches have been proposed to solve this problem. This work proposes a dual-loop control strategy. The inner loop damps the vibration of the system, eliminating the limit cycle due to nonlinear friction. The outer loop achieves the desired velocity with a fast time response. The optimal tuning of the control parameters is carried out with a multi-method ensemble meta-heuristic, the Coral Reefs Optimisation algorithm with Substrate Layer (CRO-SL). It is an evolutionary-type algorithm that combines different search strategies within a single population, obtaining a robust, high-performance algorithm to tackle hard optimisation problems. An application example based on a real nonlinear dynamics model of a drill-string illustrates that the controller optimised by the CRO-SL achieves excellent performance in terms of stick-slip vibrations cancellation, fast time response, robustness to system parameter uncertainties and chattering phenomenon prevention. [ABSTRACT FROM AUTHOR]

In recent years, reinforcement learning (RL) techniques have achieved great success in many different applications. However, their heavy reliance on complex deep neural networks makes most RL models uninterpretable, limiting their application in domains where trust and security are important. To address this challenge, we propose MENS-DT-RL, an algorithm capable of constructing interpretable models for RL via the evolution of decision tree (DT) models. MENS-DT-RL uses a multi-method ensemble algorithm to evolve univariate DTs, guiding the process with a fitness metric that prioritizes interpretability and consistent high performance. Three different initializations for the MENS-DT-RL are proposed, including the use of Imitation Learning (IL) techniques, and a novel pruning approach that reduces solution size without compromising performance. To evaluate the proposed approach, we compare it with other models from the literature on three benchmark tasks from the OpenAI Gym library, as well as on a fertilization problem inspired by real-world crop management. To the best of our knowledge, the proposed scheme is the first to solve the Lunar Lander benchmark with both interpretability and a high confidence rate (90% of episodes are successful), as well as the first to solve the Mountain Car environment with a tree of only 7 nodes. On the real-world task, the proposed MENS-DT-RL is able to produce solutions with the same quality as deep RL policies, with the added bonus of interpretability. We also analyze the best solutions found by the algorithm and show that they are not only interpretable but also diverse in their behavior, empowering the end user with the choice of which model to apply. Overall, the findings show that the proposed approach is capable of producing high-quality transparent models for RL, achieving interpretability without losing performance. • A multi-method ensemble evolutionary algorithm is proposed for Interpretable RL. • Interpretability is achieved by using Decision Trees as RL agents. • Imitation Learning is leveraged to jumpstart the evolutionary algorithm. • A novel method is proposed to prune DTs in RL environments. • State-of-the-art results are obtained for several RL benchmarks. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of users assignment to evolved NodeBs in long-term evolution mobile networks. We propose a model that aims at assigning users by minimizing a function called download time of the complete system, defined as the minimum time required for all the users in the system to complete their downloads. This strategy helps the network use its resources in a more balanced way, by assigning users from cells (which otherwise would be overloaded when using a simple signal-to-interference-plus-noise ratio-based approach) to others with less load. The user-cell association problem is, in general, a combinatorial NP hard problem. Although other approaches indirectly tackle this complexity by transforming the problem into another whose optimal solution is less difficult (but still with very high computational cost), we tackle directly our download time of the complete system minimization problem by finding an approximate solution provided by a specific implementation of an evolutionary algorithm, which is based on a long-term evolution domain-specific initial population along with fully tailored operators adapted to this assignment problem. A variety of experiments in different realistic scenarios point out that the proposed method outperforms the conventional signal-to-interference-plus-noise ratio-based and load balancing approaches, specially in urban and dense urban environments (where the assignment is more critical in terms of capacity). We have also tested our approach in simple scenarios that mimic heterogeneous networks and ultra-dense networks in which the number of users is similar to that of nodes, proving its beneficial properties. Our method is aimed at the design/dimensioning stage and not to the operation stage, and in this sense, it is an off-line algorithm that could be useful to make better decisions on cost-effective network dimensioning and upgrading. [ABSTRACT FROM AUTHOR]