Engineering optimization problems are often nonlinear, high-dimensional, and constrained, making them challenging for conventional optimization techniques. Although L-SHADE, an adaptive differential evolution (DE) algorithm with success-history based parameter adaptation, has demonstrated competitive performance, it still suffer from limited population diversity and weak local exploitation, leading to an imbalance between exploration and exploitation in complex optimization. To address these limitations, this paper proposes LCO-LSHADE-GSRL, a novel DE variant that enhances both global exploration and local exploitation capabilities. The proposed algorithm integrates three key components: (1) a Logistic Chaos Orthogonal Initialization mechanism that improves initial population diversity and ensures uniform coverage of the search space. (2) a GAN-driven Specular Reflection Learning (SRL) mechanism that effectively escapes from local optima. (3) a design that adapts effectively to constrained optimization scenarios. Comprehensive experiments conducted on the CEC 2019 and 2022 benchmark suites demonstrate that LCO-LSHADE-GSRL exhibits superior convergence performance, solution accuracy, and robustness compared to L-SHADE, LSHADE-cnEpSin, and WOA, GJO, PO, PIMO, and CDO. Furthermore, in three real-world engineering problems–speed reducer, step-cone pulley, and hydrostatic thrust bearing, which reduces system weight and power loss while satisfying all design constraints. These results demonstrate its potential for solving complex engineering optimization tasks with high reliability and efficiency.

Genetic Programming (GP) is a promising evolutionary machine learning technique for image classification, known for its ability to evolve flexible, effective, and interpretable models. However, the high computational cost of fitness evaluations in evolutionary learning restricts its practical applications. While Surrogate models offer efficient approximations for costly fitness evaluations, their application in GP-based image classification remains in its early stages, facing challenges such as handing flexible tree-based representations with variable lengths, designing an effective surrogate, and the limited performance of a single surrogate across various image classification tasks. To address these issues, this paper proposes an ensemble surrogate-assisted GP approach to image classification. The new approach constructs one global surrogate model to explore broad areas and three local surrogate models within specific subspaces to exploit local regions, enabling more accurate predictions of GP individuals’ fitness. Moreover, a dynamic weighting strategy is developed to assign different weights to the base surrogate models in the ensemble, improving prediction accuracy. Additionally, the proposed approach refines the surrogate training set1 construction method, previously limited to single-tree GP, enabling it to accelerate both single-tree and multi-tree GP 2 for image classification. Experimental results on five datasets of varying difficulty demonstrate that the new ensemble surrogate method significantly reduces the number of expensive fitness evaluations of both single-tree and multi-tree GP-based image classification methods while achieving competitive performance. The comparisons with other state-of-the-art methods also confirm its effectiveness.

In practical scenarios, there may be solutions in the decision space with close objective values but located far apart, a characteristic known as multimodal multiobjective problems (MMOPs). While most multimodal multiobjective evolutionary algorithms (MMEAs) focus on finding global Pareto optimal solution sets (PSs) and local PSs demonstrating satisfactory convergence performance, decision-makers in real-world scenarios are often also interested in local PSs that exhibit strong robustness. In this study, we propose several benchmark functions in which the global and local PSs have varying levels of robustness. Then, we introduce an innovative dual-population evolutionary algorithm, termed GLR-MMEA, designed to simultaneously find both global PSs and local PSs with strong robustness. In GLR-MMEA, the convergence population focuses on identifying global PSs, providing convergence information to the diversity population. Meanwhile, the diversity population manages the detection of both global PSs and local PSs with strong robustness. In the process of updating the diversity population, a robustness indicator is proposed to access the robustness of solutions. Furthermore, a selection mechanism founded on this robustness indicator is applied to identify local PSs with high robustness. The experimental results show that GLR-MMEA performs competitively against other leading MMEAs in working on the selected benchmark functions.

Imbalanced datasets are a major challenge in industrial fault diagnosis because the majority of data belong to the non-fault class, while fault instances constitute a small minority. Genetic Programming (GP) has shown great potential in handling imbalanced classification tasks due to its ability to evolve classifiers and automatically optimize decision rules. Traditional GP methods for imbalanced classification often rely on fixed decision thresholds (e.g., 0 or 0.5). However, such thresholds fail to adapt to varying data distributions, resulting in limited accuracy in fault diagnosis. While threshold-free methods, such as GP using the Area Under the Curve (AUC) and its variants as fitness functions, have demonstrated effectiveness, practical applications in industrial systems often require explicit thresholds to generate accurate class labels. This paper introduces a GP-based approach with a simplified AUC variant as the fitness function and a dynamic threshold search mechanism. By adaptively optimizing thresholds during evolution, the method improves minority class detection. Experiments on public fault diagnosis datasets with varying imbalance ratios demonstrate that the proposed approach consistently outperforms traditional GP methods.

Fish image classification is a very important part of intelligent aquaculture development. However, fish image classification is challenging due to large intra-class variation and high inter-class similarity, especially when the quality of the images is low. To solve these challenges, a flexible region detection-based genetic programming approach with a new terminal set, FGPN, is proposed for fish classification tasks with low-quality images. The proposed approach FGPN could automatically choose suitable terminals, detect key regions, extract multiple types of feature, as well as combine global features and local features to address the classification tasks. Compared with seven benchmark methods, including two GP-based methods and five CNN-based methods, the proposed approach FGPN achieves significantly better performance in most comparisons on three real-world datasets.

Hyperspectral image classification is effective for obtaining high-precision crop distribution maps. However, learning discriminative features from hyperspectral images to improve classification accuracy remains a highly focused research topic. Existing methods often fail to fully integrate feature selection and feature construction, which may result in learned feature sets with lower information levels and increase the risk of overfitting. To address these issues, this paper proposes a two-stage approach that combines feature selection and feature construction for hyperspectral image classification. In the first stage, feature selection is used to remove redundant features and obtain a high-quality feature subset. In the second stage, feature construction is employed to generate more discriminative high-level features, aiming to find the optimal combination of high-level and original features. Comparative experiments on four hyperspectral image datasets demonstrate that the proposed approach significantly outperforms nine baseline methods in crop classification tasks.

Skin cancer images frequently contain substantial noise, which poses challenges for effective feature extraction and classification. Although existing genetic programming (GP)-based methods exhibit adaptability to diverse tasks, they frequently lack dedicated mechanisms to effectively address noise. To overcome this limitation, this paper develops GPFF (genetic programming with a flexible filtering layer), a novel approach that reduces noise and enhances the extraction of meaningful and diverse feature representations. A novel program structure is proposed, incorporating a flexible filtering layer to enhance the reliability of feature extraction by effectively reducing noise. The flexible filtering layer incorporates a variety of image filtering functions, which capture critical image characteristics across multiple domains. By flexibly selecting and combining these filtering functions, GPFF maintains robustness across various datasets. Extensive experiments on four skin cancer datasets demonstrate that GPFF consistently outperforms five traditional feature extraction methods and three GP-based methods in most cases. Further analysis shows that the flexible filtering layer improves classification performance while achieving effective feature learning without significantly increasing computational costs, underscoring its practicality for skin cancer image classification tasks.

Structural network control principles provided novel and efficient clues for the optimization of personalized drug targets (PDTs) related to state transitions of individual patients. However, most existing methods focus on one subnetwork or module as drug targets through the identification of the minimal set of driver nodes and ignore the state transition capabilities of other modules with different configurations of drug targets [i.e. multimodal drug targets (MDTs)] embedding the knowledge of previous drug targets (i.e. multiobjective optimization). Therefore, a novel multimodal multiobjective evolutionary optimization framework (called MMONCP) is proposed to optimize PDTs with network control principles. The key points of MMONCP are that a constrained multimodal multiobjective optimization problem is formed with discrete constraints on the decision space and multimodality characteristics, and a novel evolutionary algorithm denoted as CMMOEA-GLS-WSCD is designed by combining a global and local search strategy and a weighting-based special crowding distance strategy to balance the diversity of both objective and decision space. The experimental results on three cancer genomics data from The Cancer Genome Atlas indicate that MMONCP achieves a higher performance including algorithm convergence and diversity, the fraction of identified MDTs, and the area under the curve score than advanced algorithms. Additionally, MMONCP can detect the early state from the difference between the target activity and toxicity of MDTs and provide early treatment options for cancer treatment in precision medicine.

Data-efficient image classification, which focuses on achieving accurate classification performance with limited labeled data, has garnered significant attention. Genetic programming (GP) has achieved impressive progress in image classification, particularly in scenarios involving small amounts of labeled data. GP research typically focuses on designing tree-based model representations to learn useful image features for classification. However, most GP methods are proposed for gray-scale images and ignore the color features. Furthermore, the existing GP methods typically learn features on a single scale/resolution, restricting potential accuracy enhancements. To address these issues, this paper proposes a new multi-tree GP In single-tree GP (or simply GP), each individual consists of a single tree. In contrast, in multi-tree GP, each individual comprises multiple trees. representation for image feature learning and classification. In each individual, three trees are included to extract discriminative features from the red, green, and blue channels of the image. With the new image resizing layer in the tree representation, the proposed approach can achieve multi-scale feature extraction, i.e., flexibly learning fine-grained details and coarse-grained structures in the image, improving the classification performance. In addition, since a limitation of GP is premature convergence due to a decline in population diversity, this paper develops a hybrid parent selection method consisting of tournament and lexicase selection to increase population diversity, find the best individual, and improve classification accuracy. The experiments on six image classification datasets indicate that the proposed approach outperforms state-of-the-art neural network-based and GP-based methods in almost all comparisons. Further analyses demonstrate the effectiveness of each component and the potentially high interpretability of the proposed approach.

Biological markers (i.e., biomarkers) are the key to predicting disease states and revealing the molecular mechanisms in precision medicine of complex diseases (e.g., cancer). With the advancement of high-throughput sequencing technology, there has been a significant increase in the volume and diversity of known disease omics data, where many methods have been developed to identify potential disease biomarkers (DBs) for mining the complex dynamics. As emerging artificial intelligence techniques, evolutionary computation (EC) has found extensive application in the identification of DBs, making significant achievements in mining disease omics data. However, there is currently no survey or analysis available of the existing EC methods to identify DBs on the disease omics data, resulting in missed opportunities to enhance performance and achieve successful applications in precision medicine. This article aims to present a comprehensive overview of the latest EC methods for mining the dynamics of DBs, including the summary of biomolecular omics datasets, the classification of the EC methods for DB discovery, and performance comparisons of the typical EC methods. Additionally, this article discusses challenges and potential future directions of the EC methods in the identification of DBs, providing directions and prospects for future research.

In multiobjective feature selection, different feature subsets with the same number of selected features can achieve identical classification accuracy, meaning that it is a multimodal optimization problem. To effectively search for multimodal feature subsets within the vast search spaces of high-dimensional datasets, it is crucial to adopt reasonable encoding and search methods. Generally, applying a uniform evolutionary operator based on a single encoding method across the entire feature space is inefficient and prone to falling into local optima. To address the above issues, this article proposes a multimodal multiobjective feature selection method based on a joint encoding mechanism that combines discrete encoding and continuous encoding. It provides new perspectives to solve the high-dimensional feature selection problem from encoding methods to search operators. First, the search space is divided into a discrete encoding region and a continuous encoding region based on the knee points of feature importance ranking curve. A tailored initialization strategy is used to obtain the initial population for joint encoding. Second, an adaptive niche strategy based on three priorities is proposed, which ensures the similarity of individuals within a niche and the difference between niches. In addition, different search operators are cooperated with the two encoding strategies, respectively, to achieve effective and efficient search. The experimental results on 24 datasets show that the proposed algorithm achieves a better-classification performance than the state-of-the-art feature selection methods.

Fine-grained image classification (FGIC) is an important computer vision task with many real-world applications. However, FGIC is challenging due to intra-class variations and inter-class similarities, especially when there is limited training data. To address these challenges, a new genetic programming approach with flexible region detection, GP-RD, is proposed for different FGIC tasks, i.e., flower and fish classification tasks. The proposed GP-RD approach can automatically highlight the object, detect regions of interest, extract effective features, and combine global, local, and/or color features for classification. The performance of GP-RD is evaluated on flower and fish classification tasks within the FGIC domain, utilizing datasets with varying classes. In comparison with seven benchmark methods, GP-RD achieves significantly better performance in most comparisons. Further analysis demonstrates the interpretability, effectiveness, and efficiency of the proposed approach.

As a pivotal technique in intelligent education systems, cognitive diagnosis (CD) serves to reveal students’ knowledge proficiency for better tackling subsequent tasks. Unfortunately, due to pursuing high model interpretability, existing manually designed models for CD often hold simplistic architectures, which cannot cope with intricate data in modern education platforms. Furthermore, the bias of human design limits the emergence of novel and effective CD models (CDMs). To develop interpretable and more effective models, thus this article proposes an evolutionary multiobjective neural architecture search (NAS) approach for CD. Specifically, we first adopt a comprehensive search space for the NAS task of CD: all candidate models can be encompassed by a general model that deals with three distinct types of inputs. Then, an innovative model interpretability objective is devised to formulate the architecture search task as a bi-objective optimization problem (BOP). To solve the BOP, we employ a multiobjective genetic programming (MOGP) as the search strategy to explore the search space. To make the employed MOGP search well, all architectures are first encoded by trees for easy optimization, and we devise a genetic operation and a population initialization strategy to expedite its convergence. Finally, the proposed approach is actually an MOGP-based NAS approach for CD. Extensive experiments show that CDMs searched by the proposed approach exhibit significantly better performance than existing models and hold as good interpretability as handcrafted models. Besides, the effectiveness of the proposed MOGP search strategy, the devised objective, and tailored strategies are validated.

Malignant melanoma is a very deadly form of skin cancer and early diagnosis can significantly reduce the mortality rate. Many computer-aided diagnosis (CAD) systems have been developed as second opinion diagnostic aids to assist dermatologists in diagnosing malignant melanoma. However, traditional CAD systems often require domain knowledge for feature extraction, while neural network-based CAD systems require specialized knowledge for designing network structures and often have poor interpretability. In this article, we propose a new skin cancer CAD system based on genetic programming (GP) to automatically learn effective features for classification with strong interpretability. The approach can automatically evolve variable-length models to extract informative features for describing skin cancer images based on a relatively simple program structure, a new function set, and a terminal set. In addition, compared with other GP methods, this approach employs a newly proposed duplicate subtree removing mechanism, which can effectively prevent the duplication of features, thereby simplifying the model and enhancing its interpretability. The proposed approach has been examined on five real-world skin cancer classification tasks. The results suggest that the proposed approach achieves better performance than GP-based, neural network-based feature learning comparison methods and traditional comparison methods in most cases. Further analysis shows that the proposed approach has employed a smaller tree structure and can automatically evolve/learn models with potentially high interpretability.

Road extraction from remote sensing images is crucial for various applications, such as urban planning and traffic management. Exiting road extraction methods face limitations, such as the need to improve accuracy or high requirements for training data. To address these limitations, this paper proposes a two-stage genetic programming (GP) approach to feature construction and ensemble evolving for road extraction from remote sensing images. The proposed approach can automatically evolve interpretable solutions with small training data and achieve end-to-end road extraction from remote sensing images. In the first stage, a feature construction strategy is designed to construct high-level features from multiple views, respectively, based on the type of features. In the second stage, a novel GP individual representation is proposed to allow the new method to combine the high-level features of different types, automatically generate an ensemble of base classification methods and select their parameters for classification. The proposed approach is examined on eleven road extraction tasks from the Deepglobe dataset, and compared with seven traditional methods, nine GP-based methods, and four neural network methods. The experimental results demonstrate the effectiveness of the proposed approach in enhancing accuracy and generalization capabilities. Furthermore, the visualization analysis shows the good interpretability of solution/model of the proposed approach.

Remote sensing scene classification is a vital task in remote sensing image analysis with significant application potential. In recent years, convolutional neural network (CNN)-based methods have shown remarkable promise in classifying remote sensing scene images. However, these methods often require extensive trial and error and rely heavily on expert knowledge. To address these challenges, this article proposes a novel neural architecture search (NAS) approach that automatically designs CNNs for remote sensing scene classification. Specifically, an evolutionary algorithm (EA) is employed to search for well-structured basic modules, which are then combined to construct a new architecture. To further enhance the search process, a new population generation strategy is introduced to promote diversity and mitigate premature convergence. Additionally, a random forest-based selection mechanism is utilized to identify high-quality individuals based on estimated fitness values, effectively reducing computational complexity. The proposed approach is evaluated on three benchmark remote sensing scene datasets and compared with several widely used CNNs. The experimental results demonstrate that the proposed approach can discover CNN architectures that not only surpass state-of-the-art performance but also achieve this with fewer parameters and lower search cost.

In constrained multimodal multiobjective optimization problems (CMMOPs), the existence of discrete and confined feasible regions bring great challenges to current multiobjective optimization evolutionary algorithms (MOEAs). To address these challenges, this article proposes a constrained multimodal multiobjective differential evolution algorithm, which incorporates a similar-niching-based reproduction operator and a novel environmental selection mechanism. The proposed algorithm initiates by segregating the population into distinct niches, thereby promoting independent evolution within each niche. This segmentation enhances the exploration of multiple discrete feasible regions, thus improving the capacity to find diverse Pareto optimal solutions. Moreover, the algorithm selects the most similar niche to collaboratively generate solutions, further enhancing its ability to generate effective feasible solutions. To improve the diversity within the population, the proposed environmental selection mechanism gives preference to solutions that enhance the distribution of the next-generation population. By considering the diversity in both two spaces, the population retains more pareto optimal solutions. Based on the Friedman test results of the comparison experiment with other representative algorithms and the champion algorithm of the CEC2023 CMMOPs competition, the proposed algorithm attained the top ranking, thereby reinforcing its demonstrated superiority. Meanwhile, the proposed algorithm is used to solve the constrained multimodal multiobjective location selection problem and results show its superiority.

For expensive constrained optimization problems (ECOPs), the evaluation of the objective and constraints are both expensive. Due to the low computational cost of the surrogate model and the excellent search capabilities of evolutionary algorithms, surrogate-assisted evolutionary algorithms (SAEAs) have become a popular approach for solving ECOPs. When solving ECOPs, the errors in the objective and constraint surrogates will inevitably mislead the direction of evolution, making it difficult to find feasible solutions and avoid local optima. To defeat this issue, we propose an SAEA capable of adjusting the evolutionary direction to search in the correct direction as much as possible. Specifically, the correlation between objective and constraint is first analyzed, and then adaptive adjustments are made based on this correlation to revise the evolutionary direction throughout the three stages of the evolutionary process. For reproduction, an offspring enhanced generation strategy is proposed to generate promising and diverse offspring. For sampling, a dynamic infill sampling criterion is designed to select the most suitable solutions for expensive evaluations, thereby accelerating convergence. Finally, an adaptive environment selection strategy is designed to choose parents with more potential for improvement. The proposed method is evaluated on commonly used benchmark test functions and four engineering examples, with experimental results indicating its superior performance compared to other advanced methods.

Genetic programming (GP) hyper-heuristic method has been extensively studied to solve multiple dynamic job shop scheduling tasks by generating an effective heuristic for each task simultaneously. However, a fundamental question has not been answered. Do we need to customize a specific heuristic for each task? To fill this research gap, we propose to generate a single heuristic for handling multiple tasks. Without designing complex evolution mechanisms, only during the evaluation process of GP, the fitness of a heuristic is evaluated by multiple tasks. Since there are multiple tasks, a heuristic has multiple objective values. A rank aggregation (RA) fitness evaluation strategy is designed to convert multiple objective values of multiple tasks into a fitness value for a single heuristic. To validate the effectiveness of the generated solution and the proposed RA strategy, we design multitask scenarios that encompass tasks with diverse objectives, utilization levels, and maximum operation times. The results demonstrate that the performance of the single heuristic generated in multitask scenarios is comparable to solutions generated by GP using the single-task learning paradigm, meaning that with an appropriate training method, GP can generate a heuristic with good generality.

Crop classification is an important task in remote sensing image analysis. To effectively classify crops, it is necessary to extract or obtain a set of effective features from raw pixels. However, existing methods have several limitations, including poor interpretability of the learned models and the requirements of sufficient training data and domain expertise. To address this, this paper develops a two-stage approach using Genetic Algorithm (GA) and Genetic Programming (GP) to automatically learn a feature set that can effectively classify crops using remote sensing images. In the first stage of the new approach, a GP method is applied to automatically construct a set of high-level features by evolving tree-based solutions. In the second stage, a GA method is employed to select a small subset of features from the constructed features and the original features by removing redundant ones. The performance of the new approach is evaluated on three datasets in two scenarios, i.e., classifying four main crop types and all crop types, respectively. The results demonstrate that the new approach achieves more accurate crop classification compared with eight competitive methods.

Genetic programming (GP) has shown promising results in image classification in the last decade. However, most existing GP-based image classification methods often have a complex tree/program structure and a large search space, which may lead to poor performance. To address this, this paper develops a two-stage-based GP approach to automatically evolving solutions/ensembles for image classification. In the new approach, the process of constructing an image classification solution is divided into two stages, i.e., evolving small building blocks for feature extraction and evolving ensembles of classifiers by reusing these blocks. Accordingly, at each stage, a simple tree structure can be designed to facilitate the search. In the first stage, a simple block representation and a new search mechanism including a population updating strategy are developed to evolve diverse and effective blocks. In the second stage, a small set of diverse blocks are selected and transformed into primitives, which produces a new tree representation to evolve ensembles of classifiers for image classification. The new designs allow the proposed approach to construct sufficiently but not over complex solutions for difficult tasks by using/searching small trees. The new approach outperforms most GP-based and non-GP-based comparison methods on five image datasets including CIFAR10, Fashion_MNIST and SVHN. Deep analysis is conducted to provide more insights into the proposed approach.

Data-efficient image classification is a challenging task that aims to solve image classification using small training data. Neural network-based deep learning methods are effective for image classification, but they typically require large-scale training data and have major limitations such as requiring expertise to design network architectures and having poor interpretability. Evolutionary deep learning is a recent hot topic that combines evolutionary computation with deep learning. However, most evolutionary deep learning methods focus on evolving architectures of neural networks, which still suffers from limitations such as poor interpretability. To address this, this paper proposes a new genetic programming-based evolutionary deep learning approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. It can learn different types of image features from colour or gray-scale images, and construct effective and diverse ensembles for image classification. A flexible multi-layer representation enables the new approach to automatically construct shallow or deep models/trees for different tasks and perform effective transformations on the input data via multiple internal nodes. The new approach is applied to solve five image classification tasks with different training set sizes. The results show that it achieves better performance in most cases than deep learning methods for data-efficient image classification. A deep analysis shows that the new approach has good convergence and evolves models with high interpretability, different lengths/sizes/shapes, and good transferability.

Genetic programming (GP) has achieved promising performance in image classification. However, GP-based methods usually require a long computation time for fitness evaluations, posing a challenge to real-world applications. Surrogate models can be efficiently computable approximations of expensive fitness evaluations. However, most existing surrogate methods are designed for evolutionary computation techniques with a vector-based representation consisting of numerical values, thus cannot be directly used for GP with a tree-based representation consisting of functions/operators. The variable sizes of GP trees further increase the difficulty of building the surrogate model for fitness approximations. To address these limitations, we propose a new surrogate-assisted GP approach including global and local surrogate models, which can accelerate the evolutionary learning process and achieve competitive classification performance simultaneously. The global surrogate model can assist GP in exploring the entire search space, while the local surrogate model can speed up convergence and further improve performance. Furthermore, a new surrogate training set is constructed to assist in establishing the relationship between the GP tree and its fitness, and effective surrogate models can be built accordingly. Experimental results on ten datasets of varying difficulty show that the new approach significantly reduces the computational cost of the GP-based method without sacrificing the classification accuracy. The comparisons with other state-of-the-art methods also demonstrate the effectiveness of the new approach. Further analysis reveals the significance of the global and local surrogates and the new surrogate training set on improving or maintaining the performance of the proposed approach while reducing the computational cost.

Feature construction has shown promise in improving the accuracy of crop classification by constructing high-level features. However, current feature construction methods often rely on domain knowledge and have a limited interpretability of the solutions. To address this, this study proposes a new genetic programming (GP) approach to automatically evolve solutions with high interpretability that can construct high-level features for crop classification from hyperspectral images. A flexible representation of multiple trees is proposed in the proposed GP approach to construct various types of high-level features from the original ones, simultaneously. To improve the search ability, a new offspring generation method is developed to dynamically guide the evolution of the population while improving the diversity of the population. The new approach wraps with three classification algorithms, i.e., support vector machine (SVM), naive Bayes (NB), and k-nearest neighbor (KNN), for crop classification on three datasets with different difficulties and tasks. The results demonstrate that the features constructed by the new approach can effectively distinguish different crop categories. The new approach achieves better performance than the compared GP-based method, classic methods, and deep learning methods in crop classification using hyperspectral images. Importantly, the proposed approach shows the high interpretability of the constructed features.

Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.

Genetic programming (GP) has been applied to feature learning for image classification and achieved promising results. However, many GP-based feature learning algorithms are computationally expensive due to a large number of expensive fitness evaluations, especially when using a large number of training instances/images. Instance selection aims to select a small subset of training instances, which can reduce the computational cost. Surrogate-assisted evolutionary algorithms often replace expensive fitness evaluations by building surrogate models. This article proposes an instance selection-based surrogate-assisted GP for fast feature learning in image classification. The instance selection method selects multiple small subsets of images from the original training set to form surrogate training sets of different sizes. The proposed approach gradually uses these surrogate training sets to reduce the overall computational cost using a static or dynamic strategy. At each generation, the proposed approach evaluates the entire population on the small surrogate training sets and only evaluates ten current best individuals on the entire training set. The features learned by the proposed approach are fed into linear support vector machines for classification. Extensive experiments show that the proposed approach can not only significantly reduce the computational cost but also improve the generalisation performance over the baseline method, which uses the entire training set for fitness evaluations, on 11 different image datasets. The comparisons with other state-of-the-art GP and non-GP methods further demonstrate the effectiveness of the proposed approach. Further analysis shows that using multiple surrogate training sets in the proposed approach achieves better performance than using a single surrogate training set and using a random instance selection method.

Feature learning is a promising approach to image classification. However, it is difficult due to high image variations. When the training data are small, it becomes even more challenging, due to the risk of overfitting. Multitask feature learning has shown the potential for improving generalization. However, existing methods are not effective for handling the case that multiple tasks are partially conflicting. Therefore, for the first time, this article proposes to solve a multitask feature learning problem as a multiobjective optimization problem by developing a genetic programming approach with a new representation to image classification. In the new approach, all the tasks share the same solution space and each solution is evaluated on multiple tasks so that the objectives of all the tasks can be optimized simultaneously using a single population. To learn effective features, a new and compact program representation is developed to allow the new approach to evolving solutions shared across tasks. The new approach can automatically find a diverse set of nondominated solutions that achieve good tradeoffs between different tasks. To further reduce the risk of overfitting, an ensemble is created by selecting nondominated solutions to solve each image classification task. The results show that the new approach significantly outperforms a large number of benchmark methods on six problems consisting of 15 image classification datasets of varying difficulty. Further analysis shows that these new designs are effective for improving the performance. The detailed analysis clearly reveals the benefits of solving multitask feature learning as multiobjective optimization in improving the generalization.

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyze and understand the visual content of images. However, image-related tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this article provides a comprehensive survey covering all essential EC approaches to important image analysis tasks, including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary CV (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarized to provide further guidelines and opportunities for future research.

Extracting effective features from images is crucial for image classification, but it is challenging due to high variations across images. Genetic programming (GP) has become a promising machine-learning approach to feature learning in image classification. The representation of existing GP-based image classification methods is usually the tree-based structure. These methods typically learn useful image features according to the output of the GP program’s root node. However, they are not flexible enough in feature learning since the features produced by internal nodes of the GP program have seldom been directly used. In this article, we propose a new image classification approach using GP with a new program structure, which can flexibly reuse features generated from different nodes, including internal nodes of the GP program. The new method can automatically learn various informative image features based on the new function set and terminal set for effective and efficient image classification. Furthermore, instead of relying on a predefined classification algorithm, the proposed approach can automatically select a suitable classification algorithm based on the learned features and conduct classification simultaneously in a single evolved GP program for an image classification task. The experimental results on 12 benchmark datasets of varying difficulty suggest that the new approach achieves better performance than many state-of-the-art methods. Further analysis demonstrates the effectiveness and efficiency of the flexible feature reuse in the proposed approach. The analysis of evolved GP programs/solutions shows their potentially high interpretability.

Being able to learn discriminative features from low-quality images has raised much attention recently due to their wide applications ranging from autonomous driving to safety surveillance. However, this task is difficult due to high variations across images, such as scale, rotation, illumination, and viewpoint, and distortions in images, such as blur, low contrast, and noise. Image preprocessing could improve the quality of the images, but it often requires human intervention and domain knowledge. Genetic programming (GP) with a flexible representation can automatically perform image preprocessing and feature extraction without human intervention. Therefore, this study proposes a new evolutionary learning approach using GP (EFLGP) to learn discriminative features from images with blur, low contrast, and noise for classification. In the proposed approach, we develop a new program structure (individual representation), a new function set, and a new terminal set. With these new designs, EFLGP can detect small regions from a large input low-quality image, select image operators to process the regions or detect features from the small regions, and output a flexible number of discriminative features. A set of commonly used image preprocessing operators is employed as functions in EFLGP to allow it to search for solutions that can effectively handle low-quality image data. The performance of EFLGP is comprehensively investigated on eight datasets of varying difficulty under the original (clean), blur, low contrast, and noise scenarios, and compared with a large number of benchmark methods using handcrafted features and deep features. The experimental results show that EFLGP achieves significantly better or similar results in most comparisons. The results also reveal that EFLGP is more invariant than the benchmark methods to blur, low contrast, and noise.

Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask learning problem because different tasks may have a similar feature space. Genetic programming (GP) has been successfully applied to image feature learning for classification. However, most of the existing GP methods solve one task, independently, using sufficient training data. No multitask GP method has been developed for image feature learning. Therefore, this article develops a multitask GP approach to image feature learning for classification with limited training data. Owing to the flexible representation of GP, a new knowledge sharing mechanism based on a new individual representation is developed to allow GP to automatically learn what to share across two tasks and to improve its learning performance. The shared knowledge is encoded as a common tree, which can represent the common/general features of two tasks. With the new individual representation, each task is solved using the features extracted from a common tree and a task-specific tree representing task-specific features. To find the best common and task-specific trees, a new evolutionary search process and fitness functions are developed. The performance of the new approach is examined on six multitask learning problems of 12 image classification datasets with limited training data and compared with 17 competitive methods. The experimental results show that the new approach outperforms these comparison methods in almost all the comparisons. Further analysis reveals that the new approach learns simple yet effective common trees with high effectiveness and transferability.

Few-shot image classification (FSIC) is an important but challenging task due to high variations across images and a small number of training instances. A learning system often has poor generalization performance due to the lack of sufficient training data. Genetic programming (GP) has been successfully applied to image classification and achieved promising performance. This article proposes a GP-based approach with a dual-tree representation and a new fitness function to automatically learn image features for FSIC. The dual-tree representation allows the proposed approach to have better searchability and learn richer features than a single-tree representation when the number of training instances is very small. The fitness function based on the classification accuracy and the distances of the training instances to the class centroids aims to improve the generalization performance. The proposed approach can deal with different types of FSIC tasks with various numbers of classes and different image sizes. The results show that the proposed approach achieves significantly better performance than a large number of state-of-the-art methods on nine 3-shot and 5-shot image classification datasets. Further analysis shows the effectiveness of the new components of the proposed approach, its good searchability, and the high interpretability of the evolved solutions.

Fish image classification is an important task in the protection of precious marine resources. However, this task is difficult due to the low-quality images and the high inter-class variations across images. Most existing methods use high-quality images for classification and need domain knowledge. In this paper, we develop a genetic programming (GP) approach to automatically selecting image operators to deal with the low-quality images and extracting effective features from these images for low-quality fish image classification. To achieve this, a new program structure and a new function set are developed. With these designs, the proposed GP approach can evolve solutions that use effective filtering or restoration operators to deal with the input image, select informative regions from the fish image, and extract effective global and/or local features from the fish images. The results show that the proposed approach achieves significantly better performance than 12 benchmark methods, including a state-of-the-art GP approach, on the well-known fish image classification dataset. Further analysis shows the high interpretability of the evolved GP trees and the effectiveness of the employed image filtering or restoration operators.

Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve higher classification performance than using a single classification algorithm. However, to obtain a good ensemble, the component (base) classifiers in an ensemble should be accurate and diverse. To solve image classification effectively, feature extraction is necessary to transform raw pixels into high-level informative features. However, this process often requires domain knowledge. This article proposes an evolutionary approach based on genetic programming to automatically and simultaneously learn informative features and evolve effective ensembles for image classification. The new approach takes raw images as inputs and returns predictions of class labels based on the evolved classifiers. To achieve this, a new individual representation, a new function set, and a new terminal set are developed to allow the new approach to effectively find the best solution. More important, the solutions of the new approach can extract informative features from raw images and can automatically address the diversity issue of the ensembles. In addition, the new approach can automatically select and optimize the parameters for the classification algorithms in the ensemble. The performance of the new approach is examined on 13 different image classification datasets of varying difficulty and compared with a large number of effective methods. The results show that the new approach achieves better classification accuracy on most datasets than the competitive methods. Further analysis demonstrates that the new approach can evolve solutions with high accuracy and diversity.

Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set, and a new terminal set are developed in AFECGP to allow it to detect important subband signals and extract and construct informative features, automatically and simultaneously. More important, AFECGP can produce a flexible number of features for classification. Having the generated features, k -Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP-based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with 14 baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.

Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming (GP) with a flexible representation can find the best solution without the use of domain knowledge. This article proposes a new GP-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators, and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualization of the learned features provide deep insights on the proposed approach.

Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing GP-based methods is the high computational cost, which may limit their applications on large-scale image classification tasks. To address this, this article develops a divide-and-conquer GP algorithm with knowledge transfer (KT) and ensembles to achieve fast feature learning in image classification. In the new algorithm framework, a divide-and-conquer strategy is employed to split the training data and the population into small subsets or groups to reduce computational time. A new KT method is proposed to improve GP learning performance. A new fitness function based on log loss and a new ensemble formulation strategy are developed to build an effective ensemble for image classification. The performance of the proposed approach has been examined on 12 image classification datasets of varying difficulty. The results show that the new approach achieves better classification performance in significantly less computation time than the baseline GP-based algorithm. The comparisons with state-of-the-art algorithms show that the new approach achieves better or comparable performance in almost all the comparisons. Further analysis demonstrates the effectiveness of ensemble formulation and KT in the proposed approach.

Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy.

Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (NNs). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP achieved significantly better performance in most comparisons with 11 effectively competitive methods. The visualisation of the best program further revealed the high interpretability of the solutions found by COGP.

An ensemble consists of multiple learners and can achieve a better generalisation performance than a single learner. Genetic programming (GP) has been applied to construct ensembles using different strategies such as bagging and boosting. However, no GP-based ensemble methods focus on dealing with image classification, which is a challenging task in computer vision and machine learning. This paper proposes an automated ensemble learning framework using GP (EGP) for image classification. The new method integrates feature learning, classification function selection, classifier training, and combination into a single program tree. To achieve this, a novel program structure, a new function set and a new terminal set are developed in EGP. The performance of EGP is examined on nine different image classification data sets of varying difficulty and compared with a large number of commonly used methods including recently published methods. The results demonstrate that EGP achieves better performance than most competitive methods. Further analysis reveals that EGP evolves good ensembles simultaneously balancing diversity and accuracy. To the best of our knowledge, this study is the first work using GP to automatically generate ensembles for image classification.

Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method.

Knapsack problem is famous NP-complete problem where one has to maximize the benefit of objects in a knapsack without exceeding its capacity. In this paper, a binary bacterial foraging optimization (BBFO) is proposed to find solutions of 0/1 knapsack problems. The original BFO chemotaxis equation is modified to operate in discrete space by using a mapping function, where some new variables and parameter, i.e., binary matrix y, logistic transformation S, and limiting transformation L is built to transform the bacterial position to a binary matrix. By using this schema, the proposed BBFO model can also be easily applied in other discrete problem solving. To further validate the efficiency of the BFO-based approach, an improved version BFO named BFO with linear decreasing chemotaxis step (BFO-LDC) is used to evaluate on six different instances. Comparisons with particle swarm optimization (PSO) and original BFO are presented and discussed.