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Evolutionary neural networks (ENNs) are an adaptive approach that combines the adaptive mechanism of Evolutionary algorithms (EAs) with the learning mechanism of Artificial Neural Network (ANNs). In view of the difficulties in design and development of DNNs, ENNs can optimize and supplement deep learning algorithm, and the more powerful neural network systems are hopefully built. Many valuable conclusions and results have been obtained in this field, especially in the construction of automated deep learning systems. This study conducted a systematic review of the literature on ENNs by using the PRISMA protocol. In literature analysis, the basic principles and development background of ENNs are firstly introduced. Secondly, the main research techniques are introduced in terms of connection weights, architecture design and learning rules, and the existing research results are summarized and the advantages and disadvantages of different research methods are analyzed. Then, the key technologies and related research progress of ENNs are summarized. Finally, the applications of ENNs are summarized and the direction of future work is proposed. [ABSTRACT FROM AUTHOR]

The accurate identification of moving target types in alert areas is a fundamental task for unattended ground sensors. Considering that the seismic and sound signals generated by ground moving targets in urban areas are easily affected by environmental noise and the power consumption of unattended ground sensors needs to be reduced to achieve low-power consumption, this paper proposes a ground moving target detection method based on evolutionary neural networks. The technique achieves the selection of feature extraction methods and the design of evolving neural network structures. The experimental results show that the improved model can achieve high recognition accuracy with a smaller feature vector and lower network complexity. [ABSTRACT FROM AUTHOR]

The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks which, in spite of the great success of deep architectures, are still in use, for example, in robotic systems. The paper analyses the influence of different mechanisms incorporated into HCAE on the effectiveness of evolved neural networks and compares it with a number of rival algorithms. In order to verify the ability of HCAE to evolve effective small/mid-scale neural networks, both feed forward and recurrent, it was tested on fourteen identification problems including the two-spiral problem, which is a well-known binary classification benchmark, and on two control problems, i.e., the inverted-pendulum problem, which is a classical control benchmark, and the trajectory-following problem, which is a real problem in underwater robotics. Four other neuro-evolutionary algorithms, four particle swarm optimization methods, differential evolution, and a well-known back-propagation algorithm, were applied as a point of reference for HCAE. The experiments reported in the paper revealed that the evolutionary approach applied in the proposed algorithm makes it a more effective tool for solving the test problems than all the rivals. [ABSTRACT FROM AUTHOR]

Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics, and sentiment analysis, to name a few. It is therefore important to verify the effectiveness of the clustering algorithm in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of two popular clustering mechanisms by comparing their performance over publicly available benchmarking data sets that capture a variety of data point distributions as well as the number of attributes, especially from a computational point of view by incorporating techniques that alleviate some of the issues that plague these algorithms. Sensitivity to initialization conditions and stagnation to local minima are explored. Further, an implementation of a feedforward neural network utilizing a fully connected topology in particle swarm optimization is introduced. This serves to be a guided random search technique for the neural network weight optimization. The algorithms utilized here are studied and compared, from which their applications are explored. The study aims to provide a handy reference for practitioners to both learn about and verify benchmarking results on commonly used real-world data sets from both a supervised and unsupervised point of view before application in more tailored, complex problems.

Load forecasting/prediction is a challenging task in the energy markets that require adequate attention in generating stable and reliable load demand to deal with energy management and planning strategies. Accurate load prediction is critical for electrical power systems operations, but nonlinear loads involve high volatility. Forecasting these kinds of complex load characteristics requires highly accurate forecasting devices. It is generally accomplished by constructing models on relative details, including weather and previous data on load demand. This study proposes six decomposition-based evolutionary neural networks for city-scale and building energy forecasting. These evolutionary neural networks have also been trained using historical load data, and weather records are already considered to have a significant effect on energy usage (for example, wind speed, precipitation, dry-dew point temperature, relative humidity, clouds fraction and mean sea level perception). The network structure of the model is built and based on error estimation, trend map, and appropriate method of measuring evolutionary neural networks efficiency. The model's hidden layers and neurons network structure is selected based on recent smart models to enhance its accuracy. Several measures were used to improve performance, such as seasonal smoothing changes, coarse-graining of the humidity ratio, load-oriented day-type classification monitoring, outlier removal, and complex climate information. Results show that used models have a very high fitting accuracy and low error rate for short, medium, and long-term forecasting/planning tasks. These models have the potential to reduce overfitting issues, thereby improving load forecasting efficiency. Further, proposed models may also use for forecasting solar and wind demand. The efficiency of the model compared to the existing two models. Forecast results show superiority and efficiency in short, medium and long-term energy forecasting and offer the ability to manage unbalanced utility loads.

Evolutionary Algorithms (EA) are widely applied in Neural Architecture Search (NAS) and have achieved appealing results. Different EA-based NAS algorithms may utilize different encoding schemes for network representation, while they have the same workflow. Specifically, the first step is the initialization of the population with different encoding schemes, and the second step is the evaluation of the individuals by the fitness function. Then, the EA-based NAS algorithm executes evolution operations, e.g., selection, mutation, and crossover, to eliminate weak individuals and generate more competitive ones. Lastly, evolution continues until the max generation and the best neural architectures will be chosen. Because each individual needs complete training and validation on the target dataset, the EA-based NAS always consumes significant computation and time inevitably, which results in the bottleneck of this approach. To ameliorate this issue, this article proposes a distributed framework to boost the computation of the EA-based NAS algorithm. This framework is a server/worker model where the server distributes individuals requested by the computing nodes and collects the validated individuals and hosts the evolution operations. Meanwhile, the most time-consuming phase (i.e., individual evaluation) of the EA-based NAS is allocated to the computing nodes, which send requests asynchronously to the server and evaluate the fitness values of the individuals. Additionally, a new packet structure of the message delivered in the cluster is designed to encapsulate various network representations and support different EA-based NAS algorithms. We design an EA-based NAS algorithm as a case to investigate the efficiency of the proposed framework. Extensive experiments are performed on an illustrative cluster with different scales, and the results reveal that the framework can achieve a nearly linear reduction of the search time with the increase of the computational nodes. Furthermore, the length of the exchanged messages among the cluster is tiny, which benefits the framework expansion. [ABSTRACT FROM AUTHOR]

The accurate identification of moving target types in alert areas is a fundamental task for unattended ground sensors. Considering that the seismic and sound signals generated by ground moving targets in urban areas are easily affected by environmental noise and the power consumption of unattended ground sensors needs to be reduced to achieve low-power consumption, this paper proposes a ground moving target detection method based on evolutionary neural networks. The technique achieves the selection of feature extraction methods and the design of evolving neural network structures. The experimental results show that the improved model can achieve high recognition accuracy with a smaller feature vector and lower network complexity.

Energy-Dissipative Evolutionary Deep Operator Neural Network is an operator learning neural network. It is designed to seek numerical solutions for a class of partial differential equations instead of a single partial differential equation, such as partial differential equations with different parameters or different initial conditions. The network consists of two sub-networks, the Branch net and the Trunk net. For an objective operator G , the Branch net encodes different input functions f at a fixed number of sensors x i , i = 1 , 2 , ⋯ , m , and the Trunk net evaluates the output function at any location. By minimizing the error between the evaluated output q and the expected output G (f) (y) at the test point y , DeepONet generates a good approximation of the operator G. A key distinction in our methodology is the utilization of DeepONet for the training of the initial state, which operates as a multi-parametric operator. Further, the evolution of parameters in our model is facilitated by the Scalar Auxiliary Variable (SAV) method, leading to a formulated iterative process for the parameter's progression over time. The SAV approach is adopted to preserve essential physical properties of PDEs, particularly the Energy Dissipation Law. It introduces a kind of modified energy and establishes unconditional energy dissipation law in the discrete level. By treating the parameters of the well-trained DeepONet as a representation of the initial operator and evolving them by a dynamic system, our network can predict the accurate solution at any further time, while the training data is only the initial state. In order to validate the accuracy and efficiency of our neural networks, we provide numerical simulations of several partial differential equations, including heat equations, parametric heat equations, Allen-Cahn equations and a reaction–diffusion equation in three dimensions. • A novel evolutionary operator neural network is designed to solve PDEs. • EDE-DeepONet is capable of solving PDEs with initial and boundary conditions. • EDE-DeepONet solves the parametric PDEs. • The solution predicted by EDE-DeepONet keeps the unconditionally energy dissipation law. • Adaptive time step is employed in EDE-DeepONet to speed up the training. [ABSTRACT FROM AUTHOR]

Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system. [ABSTRACT FROM AUTHOR]

Time series forecasting (TSF) consists on estimating models to predict future values based on previously observed values of time series, and it can be applied to solve many real-world problems. TSF has been traditionally tackled by considering autoregressive neural networks (ARNNs) or recurrent neural networks (RNNs), where hidden nodes are usually configured using additive activation functions, such as sigmoidal functions. ARNNs are based on a short-term memory of the time series in the form of lagged time series values used as inputs, while RNNs include a long-term memory structure. The objective of this paper is twofold. First, it explores the potential of multiplicative nodes for ARNNs, by considering product unit (PU) activation functions, motivated by the fact that PUs are specially useful for modelling highly correlated features, such as the lagged time series values used as inputs for ARNNs. Second, it proposes a new hybrid RNN model based on PUs, by estimating the PU outputs from the combination of a long-term reservoir and the short-term lagged time series values. A complete set of experiments with 29 data sets shows competitive performance for both model proposals, and a set of statistical tests confirms that they achieve the state of the art in TSF, with specially promising results for the proposed hybrid RNN. The experiments in this paper show that the recurrent model is very competitive for relatively large time series, where longer forecast horizons are required, while the autoregressive model is a good selection if the data set is small or if a low computational cost is needed. [ABSTRACT FROM AUTHOR]

The accuracies of three different evolutionary artificial neural network (ANN) approaches, ANN with genetic algorithm (ANN-GA), ANN with particle swarm optimization (ANN-PSO) and ANN with imperialist competitive algorithm (ANN-ICA), were compared in estimating groundwater levels (GWL) based on precipitation, evaporation and previous GWL data. The input combinations determined using auto-, partial auto- and cross-correlation analyses and tried for each model are: (i) GWL and GWL; (ii) GWL, GWL and P ; (iii) GWL, GWL and E ; (iv) GWL, GWL, P and E ; (v) GWL, GWL and P where GWL, P and E indicate the GWL, precipitation and evaporation at time t, individually. The optimal ANN-GA, ANN-PSO and ANN-ICA models were obtained by trying various control parameters. The best accuracies of the ANN-GA, ANN-PSO and ANN-ICA models were obtained from input combination (i). The mean square error accuracies of the ANN-GA and ANN-ICA models were increased by 165 and 124% using ANN-PSO model. The results indicated that the ANN-PSO model performed better than the other models in modeling monthly groundwater levels. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of improving the convergence properties of evolutionary neural networks, particularly in the context of hybrid neural networks that adopt a diversity of transfer functions at their nodes, i.e.: neural diversity machines. The paper explores the potential of solution complementarity, in the context of pattern recognition problems, and focuses on its incorporation in the selection process of recombination heuristics. In a pattern recognition context, complementarity is defined as the ability of different solutions to correctly classify complementary subsets of patterns. A broad set of experiments was conducted demonstrating that solution selection based on complementarity is statistically significantly better than random selection, in a wide range of conditions, e.g.: different datasets, recombination heuristics and architectural constraints. Although the experiments demonstrated the statistical significance and robustness of the effect, they also indicated that more work is required to increase the degree of the effect and to scale-up to larger and more complex datasets. [ABSTRACT FROM AUTHOR]

In essence, back analysis is a process of system identification. Therefore, artificial neural networks represent a suitable solution methodology for this problem. To overcome the shortcomings of the neural networks and evolutionary neural networks, based on immunized evolutionary programming, a new evolutionary neural network whose architecture and connection weights simultaneously evolve is proposed. Using this new evolutionary neural network, a novel inverse back analysis for underground engineering is studied. Using a numerical example and a real engineering example, namely, an underground roadway of the Huainan coal mine in China, the accuracy of this inverse back analysis is verified. Moreover, the non-uniqueness of the solution generated by the inverse back analysis is analyzed. The results show that, using the back-calculated parameters, the computed displacements agree with the measured ones. Thus, the new inverse back analysis method is demonstrated to be a high-performance method for usage in underground engineering. Moreover, various other conclusions can be drawn: the training samples of the neural network should be collected from the results of the positive analysis by the finite element method and selected based on the orthogonal experimental design, and the precision of the back analysis using multiple parameters is worse than that using a single parameter. [ABSTRACT FROM AUTHOR]

The receiver operating characteristics (ROC) analysis has gained increasing popularity for analyzing the performance of classifiers. In particular, maximizing the convex hull of a set of classifiers in the ROC space, namely ROCCH maximization, is becoming an increasingly important problem. In this work, a new convex hull-based evolutionary multi-objective algorithm named ETriCM is proposed for evolving neural networks with respect to ROCCH maximization. Specially, convex hull-based sorting with convex hull of individual minima (CH-CHIM-sorting) and extreme area extraction selection (EAE-selection) are proposed as a novel selection operator. Empirical studies on 7 high-dimensional and imbalanced datasets show that ETriCM outperforms various state-of-the-art algorithms including convex hull-based evolutionary multi-objective algorithm (CH-EMOA) and non-dominated sorting genetic algorithm II (NSGA-II). [ABSTRACT FROM AUTHOR]

Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in this paper. From the guidance of position sensors, artificial neural network (ANN) based controllers settle the desired trajectory between current and a target point. Evolutionary algorithms were used to choose the best controller. This approach, known as Evolutionary Robotics (ER), commonly resorts to very simple ANN architectures. Although they include temporal processing, most of them do not consider the learned experience in the controller's evolution. Thus, the ER research presented in this article, focuses on the specification and testing of the ANN based controllers implemented when genetic mutations are performed from one generation to another. Discrete-Time Recurrent Neural Networks based controllers were tested, with two variants: plastic neural networks (PNN) and standard feedforward (FFNN) networks. Also the way in which evolution was performed was also analyzed. As a result, controlled mutation do not exhibit major advantages against over the non controlled one, showing that diversity is more powerful than controlled adaptation.

The ability to precisely predict behavior of a ship can be useful for different ship systems, for example, dynamic positioning, video or artillery systems. To automatically maintain a ship position and heading, the dynamic positioning system has to know future behavior of the ship as exactly as possible. The same applies to the ship video and artillery systems which to continuously track a target have to predict both the location of the target and orientation of the ship deck in relation to the target in successive points in time. In this paper, evolutionary neural networks are proposed as ship behavior predictors. To perform the task, they are supplied with the information about ship spatial orientation (Euler angles) acquired from inertial navigational systems. In experiments reported in the paper, both monolithic and modular recurrent neural networks were tested. To build them, a neuro-evolutionary method called Assembler Encoding with Evolvable Operations was applied. As the point of reference for the networks two other prediction methods were used: the first is Linear Regression with Correction, i.e. the most effective method in preliminary experiments, whereas the second is Autoregressive Integrated Moving Average, which seems to be currently the most general tool for forecasting a time series. [ABSTRACT FROM AUTHOR]

Autonomous Surface Vehicles (ASV) are robots destined for the operation at water basins like lakes, canals, harbors and even open sea. They are used for different purposes, e.g. for patrol tasks, as scouts, or as a support for Navy ships. One of the main tasks of ASV is to move along a fixed path to a destination point. To this end, an anti-collision system (ACS) has to be used with the ability to lead ASV along a path and to simultaneously avoid all additional objects present at the basin, e.g. ships, sailing boats, fishing cutters, icebergs. To perform this task, the ACS implemented as an evolutionary neural network can be used. The paper describes architecture of the neural ACS, presents two neuro-evolutionary methods used to build the system, and reports the whole process of constructing it. [ABSTRACT FROM AUTHOR]

The neuro-evolution is a domain of artificial intelligence which uses the evolutionary approach to produce artificial neural networks. There are many neuro-evolutionary methods and one of them is Assembler Encoding. This paper compares Assembler Encoding with other methods from the range of neuro-evolution and and reinforcement learning. During comparison tests, the task was to form neuro-controllers for three variants of the inverted pendulum problem. The variants differed in the amount of information supplied to each neuro-controller and in the number of poles installed on a cart. [ABSTRACT FROM AUTHOR]

The paper compares a neuro-evolutionary method called Assembler Encoding with two other methods from the area of neuro-evolution. As a testbed for the methods a variant of the predator-prey problem with Autonomous Underwater Vehicles (AUV) operating in an environment with the sea current was used. In the experiments, the task of vehicles-predators controlled with evolu-tionary neural networks was to capture a vehicle-prey behaving ac-cording to a simple deterministic strategy. All the experiments were carried out in simulation, and in order to simplify calculations in the two-dimensional environment - AUVs moved on a horizontal surface under the water. [ABSTRACT FROM AUTHOR]

Abstract: Artificial Intelligence models (AI) which computerize human reasoning has found a challenging test bed for various paradigms in many areas including financial time series prediction. Extensive researches have resulted in numerous financial applications using AI models. Since stock investment is a major investment activity, Lack of accurate information and comprehensive knowledge would result in some certain loss of investment. Hence, stock market prediction has always been a subject of interest for most investors and professional analysts. Stock market prediction is a challenging problem because uncertainties are always involved in the market movements. This paper proposes a hybrid intelligent model for stock exchange index prediction. The proposed model is a combination of data preprocessing methods, genetic algorithms and Levenberg–Marquardt (LM) algorithm for learning feed forward neural networks. Actually it evolves neural network initial weights for tuning with LM algorithm by using genetic algorithm. We also use data pre-processing methods such as data transformation and input variables selection for improving the accuracy of the model. The capability of the proposed method is tested by applying it for predicting some stock exchange indices used in the literature. The results show that the proposed approach is able to cope with the fluctuations of stock market values and also yields good prediction accuracy. So it can be used to model complex relationships between inputs and outputs or to find data patterns while performing financial prediction. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a “Divide and Conquer” approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An evolutionary search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases. [Copyright &y& Elsevier]

Abstract: The paper presents preliminary research whose main goal is to build a ship protection system. One of the elements of the system will be a sub-system responsible for capturing and destroying/neutralizing all dangerous objects occurring inside a guarded area. This task will be performed by a team of autonomous underwater vehicles. To construct a decision system for the vehicles, i.e. the system whose task is to provide high-level decisions regarding direction and velocity of move, many different methods can be applied. Examples are evolutionary neural networks and expert systems with fuzzy decision rules. To test usefulness of the above methods in a role of the decision system, experiments in a predator–prey problem were carried out. In the experiments, the task of vehicles-predators was to capture a vehicle-prey behaving by a simple deterministic strategy. The current paper is a report on all the experiments mentioned. [Copyright &y& Elsevier]

Abstract: A new method is proposed to deal with the dual-axis control of a multi-variables system with two induction motors. Investigation of resolving the cross-coupling problem of dual-axis platform is addressed by a neural net-based decoupling compensator and a sufficient condition ensuring closed-loop stability is derived. An evolutionary algorithm processing the universal seeking capability is proposed for finding the optimal connecting weights of the neural decoupling compensator and the gains of PID controllers. Extensive numerical studies verify the performance and applicability of the proposed design under a variety of operating conditions. [Copyright &y& Elsevier]

A novel temperature forecasting method has been proposed based on the evolutionary neural networks. The architecture converts the disadvantages of the backpropagation neural network to its advantage and gives better simulation results than the existing architectures. Ant colony algorithm optimized genetic algorithms are employed to evolve a no of neural networks in order to obtain the optimum network architecture and weights. Further, a context layer is added to the hidden layers of the neuro-fuzzy model to incorporate a feedback mechanism. The proposed architecture is applied to forecast temperature for New Delhi and results are analyzed. Input parameters were chosen in accordance to conditions at New Delhi and its past 10 years' weather data (2000-2010) was used to train the network. The proposed architecture was tested on 90 test cases and its results and performance analysis are shown. [ABSTRACT FROM AUTHOR]

This paper presents a new neural network encoding method called Assembler Encoding. The method was tentatively tested in the predator-prey problem. In the experiments, neural networks created by means Assembler Encoding, were used to control predators whose task was to capture a fast moving prey behaving according to a simple strategy. To compare Assembler Encoding with another neural network encoding method, in the experiments, a co-evolutionary version of simple connectivity matrix was also applied. The results of the experiments are included in the final part of the paper. [ABSTRACT FROM AUTHOR]

In a genetic algorithm, the search process maintains multiple solutions and their interactions are important to accelerate the evolution. If the pool of solutions is dominated by the single fittest individual in the early generation, there is a risk of premature convergence losing exploration capability. It is necessary to consider not only the fitness of solutions but also the similarity to other individuals. This speciation idea is beneficial to several application domains with evolutionary computation but it requires objective distance measures to calculate the similarity of individuals. It raises a challenging research issue to measure the distance between two evolutionary neural networks (ENN). In this paper, we surveyed several distance measures proposed for ENN and compared their performance for pattern classification problems with two different genetic representations (matrix-based and topology growing (NEAT) approaches). Although there was no dominant distance measure for the pattern classification problems, it showed that the behavioral distance measures outperformed the architectural one for matrix-based representation and. For NEAT, NeuroEdit showed better accuracy against compatibility distance measure. [ABSTRACT FROM AUTHOR]

Abstract: Olive (Olea europaea L.) is the main perennial Spanish crop. Soil management in olive orchards is mainly based on intensive and tillage operations, which have a great relevancy in terms of negative environmental impacts. Due to this reason, the European Union (EU) only subsidizes cropping systems which require the implementation of conservation agro-environmental techniques such as cover crops between the rows. Remotely sensed data could offer the possibility of a precise follow-up of presence of cover crops to control these agrarian policy actions, but firstly, it is crucial to explore the potential for classifying variations in spectral signatures of olive trees, bare soil and cover crops using field spectroscopy. In this paper, we used hyperspectral signatures of bare soil, olive trees, and sown and dead cover crops taken in spring and summer in two locations to evaluate the potential of two methods (MultiLogistic regression with Initial and Radial Basis Function covariates, MLIRBF; and SimpleLogistic regression with Initial and Radial Basis Function covariates, SLIRBF) for classifying them in the 400–900nm spectrum. These methods are based on a MultiLogistic regression model formed by a combination of linear and radial basis function neural network models. The estimation of the coefficients of the model is carried out basically in two phases. First, the number of radial basis functions and the radii and centres’ vector are determined by means of an evolutionary neural network algorithm. A maximum likelihood optimization method determines the rest of the coefficients of a MultiLogistic regression with a set of covariates that include the initial variables and the radial basis functions previously estimated. Finally, we apply forward stepwise techniques of structural simplification. We compare the performance of these methods with robust classification methods: Logistic Regression without covariate selection, MLogistic; Logistic Regression with covariate selection, SLogistic; Logistic Model Trees algorithm (LMT); the C4.5 induction tree; Naïve Bayesian tree algorithm (NBTree); and boosted C4.5 trees using AdaBoost.M1 with 10 and 100 boosting iterations. MLIRBF and SLIRBF models were the best discriminant functions in classifying sown or dead cover crops from olive trees and bare soil in both locations and seasons by using a seven-dimensional vector with green (575nm), red (600, 625, 650 and 675nm), and near-infrared (700 and 725nm) wavelengths as input variables. These models showed a correct classification rate between 95.56% and 100% in both locations and seasons. These results suggest that mapping covers crops in olive trees could be feasible by the analysis of high resolution airborne imagery acquired in spring or summer for monitoring the presence or absence of cover crops by the EU or local administrations in order to make the decision on conceding or not the subsidy. [Copyright &y& Elsevier]