Using Neural Networks to Automate Monitoring of Fish Stocks

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Dual-Tree Genetic Programming for Few-Shot Image Classification

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Genetic Programming for Manifold Learning: Preserving Local Topology

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A non-specialized ensemble classifier using multi-objective optimization

Ensemble classification algorithms are often designed for data with certain properties, such as imbalanced class labels, a large number of attributes, or continuous data. While high-performing, these algorithms sacrifice performance when applied to data outside the targeted domain. We propose a non-specific ensemble classification algorithm that uses multi-objective optimization instead of relying on heuristics and fragile user-defined parameters. Only two user-defined parameters are included, with both being found to have large windows of values that produce statistically indistinguishable results, indicating the low level of expertise required from the user to achieve good results. Additionally, when given a large initial set of trained base-classifiers, we demonstrate that a multi-objective genetic algorithm aiming to optimize prediction accuracy and diversity will prefer particular types of classifiers over others. The total number of chosen classifiers is also surprisingly small – only 10.14 classifiers on average, out of an initial pool of 900. This occurs without any explicit preference for small ensembles of classifiers. Even with these small ensembles, significantly lower empirical classification error is achieved compared to the current state-of-the-art. © 2020 Elsevier B.V

Evolving Scheduling Heuristics via Genetic Programming with Feature Selection in Dynamic Flexible Job Shop Scheduling

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Improved artificial bee colony algorithm for vehicle routing problem with time windows

<div><p>This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.</p></div

Deep learning model with GA-based visual feature selection and context integration

Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Convolutional Neural Network (CNN)-based deep learning models have consistently outperformed other machine learning methods on many significant image processing benchmarks. Many top-performing methods for image segmentation are also based on deep CNN models. However, deep CNN models fail to integrate global and local context alongside visual features despite having complex multi-layer architectures. We propose a novel three-layered deep learning model that learns independently global and local contextual information alongside visual features, and visual feature selection based on a genetic algorithm. The novelty of the proposed model is that One-vs-All binary class-based learners are introduced to learn Genetic Algorithm (GA) optimized features in the visual layer, followed by the contextual layer that learns global and local contexts of an image, and finally the third layer integrates all the information optimally to obtain the final class label. Stanford Background and CamVid benchmark image parsing datasets were used for our model evaluation, and our model shows promising results. The empirical analysis reveals that optimized visual features with global and local contextual information play a significant role to improve accuracy and produce stable predictions comparable to state-of-the-art deep CNN models

Genetic programming with a new representation and a new mutation operator for image classification

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Customer sorting by angles.

<p>Customer sorting by angles.</p