Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Learning with Coevolutionary Generative Adversarial Networks

It can be expensive to label images for classification. Good classifiers or high-quality images can be trained on unlabeled data with Generative Adversarial Network (GAN) methods. We use coevolutionary algorithms with Semi-Supervised GANs (SSL-GANs) that work with a few labeled and some more unlabeled images to train both a good classifier and a high-quality image generator. A spatial coevolutionary algorithm introduces diversity into the GAN training. We use a two-dimensional grid of GANs to gain discriminator loss diversity with a distributed cell-level coevolutionary algorithm. The GAN components are exchanged between neighboring cells based on performance and population-based hyperparameter tuning. The approach is demonstrated on two separate benchmark datasets, and with only a few labels, we simultaneously achieve good classification accuracy and high generated image quality score. In addition, the generated image quality and classification accuracy are competitive to state-of-the-art methods.
Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression?

The aim of Symbolic Regression (SR) is to discover interpretable expressions that accurately describe data. The accuracy of an expression depends on both its structure and coefficients. To keep the structure simple enough to be interpretable, effective coefficient optimisation becomes key. Gradient-based optimisation is clearly effective at training neural networks in Deep Learning (DL), which can essentially be viewed as large, over-parameterised expressions: in this paper, we study how gradient-based optimisation techniques as often used in DL transfer to SR. In particular, we first assess what techniques work well across random SR expressions, independent of any specific SR algorithm. We find that mini-batching and gradient-clipping can be helpful (similar to DL), while second-order optimisers outperform first-order ones (different from DL). Next, we consider whether including gradient-based optimisation in Genetic Programming (GP), a classic SR algorithm, is beneficial. On five real-world datasets, in a generation-based comparison, we find that second-order optimisation outperforms coefficient mutation (or no optimisation). However, in time-based comparisons, performance gaps shrink substantially because the computational expensiveness of second-order optimisation causes GP to perform fewer generations. The interplay of computational costs between the optimisation of structure and coefficients is thus a critical aspect to consider.
OmnImage: Evolving 1k Image Cliques for Few-Shot Learning

Few-shot learning datasets contain a large number of classes with only a few examples in each. Existing datasets may contain thousands of classes, but very simple images (e.g. handwritten characters) such that a naive baseline can perform very well. Or they may be so complex, with large within-class variation and distracting background, that they are too difficult to enable meaningful learning with so few examples. To construct a customizable dataset of consistent natural images, we assemble a new dataset with each class containing a small subset from each of the 1000 classes of ImageNet-1k. To select subsets with clear within-class consistency we use an evolutionary approach to minimize the pairwise cosine-distance between features generated by a pre-trained VGG model. We train a classifier on these evolved image cliques and find that our evolved dataset provides a greater challenge than hand written digits, but not the extreme difficulty of a non-evolved subset of ImageNet. We find that pre-training our classifier on these evolved prototypical classes significantly improves performance on classifying random subsets ImageNet (relative to pre-training on similar random-class subsets), and conjecture that these prototypical classes may be beneficial for seeding concept learning. Dataset and code is publicly available at: https://github.com/lfrati/OmnImage
Factored Particle Swarm Optimization for Policy Co-training in Reinforcement Learning

Uncertainty of the environment limits the circumstances with which any optimization problem can provide meaningful information. Multiple optimizers can combat this problem by communicating different information through cooperative coevolution. In reinforcement learning (RL), uncertainty can be reduced by applying learned policies collaboratively with another agent. Here, we propose policy Co-training with Factored Evolutionary Algorithms (CoFEA) to evolve an optimal policy for such scenarios. We hypothesize that self-paced co-training can allow factored particle swarms with imperfect knowledge to consolidate knowledge from each of their imperfect policies in order to approximate a single optimal policy. Additionally, we show how the performance of co-training swarms of RL agents can be maximized through the specific use of Expected SARSA as the policy learner. We evaluate CoFEA against comparable RL algorithms and attempt to establish limits for which our procedure does and does not provide benefit. Our results indicate that Particle Swarm Optimization (PSO) is effective in training multiple agents under uncertainty and that FEA reduces swarm and policy updates. This paper contributes to the field of cooperative co-evolutionary algorithms by proposing a method by which factored evolutionary techniques can significantly improve how multiple RL agents collaborate under extreme uncertainty to solve complex tasks faster than a single agent can under identical conditions.
Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS

To mine high-dimensional rules in Learning Classifier Systems (LCSs) through a reduction of the dimensionality of input data, this paper proposes a novel approach that indirectly learns the rules in the "latent space" based on the rewards of the reconstructed rules in the "observation space". We call this approach Learning Strategy by exploring rules in Observation space via Latent space (LS-OvL), which is based on two rule representations in the observation and latent space. Concretely, LS-OvL explores the rules by searching the latent space as the reduced dimensional input space by an autoencoder and evaluates them in the observation space by reconstructing them from the latent space. Such a design is significant because it prevents the generation of inaccurate rules during the reconstitution process from the latent space to the observation space. Through a comparison LS-OvL with the conventional learning strategy, which explores and evaluates the rules in the only latent space and reconstructs them in the observation space, the experimental results show that (1) LS-OvL outperforms the conventional learning strategy in terms of the acquired reward and the population size, and (2) LS-OvL can generate explainable and classifiable high-dimensional rules.
The Role of Audit in Mitigating Risks Associated with the Digitalization of HR Processes

The digitalization of HR processes has transformed the way organizations operate, improving efficiency and streamlining operations. However, this digital transformation also introduces a new set of risks that organizations need to address to ensure the security and integrity of their HR data.This research paper examines the crucial role of audit in mitigating these risks associated with the digitalization of HR processes. The study examines the various risks that organizations face in the digital era, including data breaches, unauthorized access, and system failures. It explores the potential vulnerabilities and challenges that emerge with the adoption of digital HR systems and highlights the need for a robust audit framework to ensure data integrity, privacy, and protection. It discusses how audit procedures and controls can help identify vulnerabilities and provide assurance over the effectiveness of controls implemented to manage these risks. The research paper also identifies key strategies and best practices that organizations can implement to effectively audit their digital HR processes and enhance risk management. The objective is to analyze how auditors can contribute to ensuring the security, reliability, and compliance of digital HR processes. The paper examines the specific risk areas associated with HR digitalization, including data privacy, cybersecurity, accuracy, and regulatory compliance. Drawing upon existing literature and case studies, the research highlights the importance of audit procedures, such as control testing, vulnerability assessments, and data analytics, in addressing these risks. It further explores the challenges auditors may encounter, such as the need for specialized knowledge and the dynamic nature of technology. The findings emphasize the need for effective auditing strategies to enhance organizational resilience against digital HR risks. This paper is expected to contribute to the academic literature on HR digitalization and provide practical insights for auditors and HR professionals to effectively navigate the risks associated with the digital transformation of HR processes
Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier Systems

This paper focuses on the impact of rule representation in Michigan-style Learning Fuzzy-Classifier Systems (LFCSs) on its classification performance. A well-representation of the rules in an LFCS is crucial for improving its performance. However, conventional rule representations frequently need help addressing problems with unknown data characteristics. To address this issue, this paper proposes a supervised LFCS (i.e., Fuzzy-UCS) with a self-adaptive rule representation mechanism, entitled Adaptive-UCS. Adaptive-UCS incorporates a fuzzy indicator as a new rule parameter that sets the membership function of a rule as either rectangular (i.e., crisp) or triangular (i.e., fuzzy) shapes. The fuzzy indicator is optimized with evolutionary operators, allowing the system to search for an optimal rule representation. Results from extensive experiments conducted on continuous space problems demonstrate that Adaptive-UCS outperforms other UCSs with conventional crisp-hyperrectangular and fuzzy-hypertrapezoidal rule representations in classification accuracy. Additionally, Adaptive-UCS exhibits robustness in the case of noisy inputs and real-world problems with inherent uncertainty, such as missing values, leading to stable classification performance.
Grammar-guided Linear Genetic Programming for Dynamic Job Shop Scheduling

Dispatching rules are commonly used to make instant decisions in dynamic scheduling problems. Linear genetic programming (LGP) is one of the effective methods to design dispatching rules automatically. However, the effectiveness and efficiency of LGP methods are limited due to the large search space. Exploring the entire search space of programs is inefficient for LGP since a large number of programs might contain redundant blocks and might be inconsistent with domain knowledge, which would further limit the effectiveness of the produced LGP models. To improve the performance of LGP in dynamic job shop scheduling problems, this paper proposes a grammar-guided LGP to make LGP focus more on promising programs. Our dynamic job shop scheduling simulation results show that the proposed grammar-guided LGP has better training efficiency than basic LGP, and can produce solutions with good explanations. Further analyses show that grammar-guided LGP significantly improves the overall test effectiveness when the number of LGP registers increases.
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces

Quality Diversity (qd) algorithms have been proposed to search for a large collection of both diverse and high-performing solutions instead of a single set of local optima. While early qd algorithms view the objective and descriptor functions as black-box functions, novel tools have been introduced to use gradient information to accelerate the search and improve overall performance of those algorithms over continuous input spaces. However a broad range of applications involve discrete spaces, such as drug discovery or image generation. Exploring those spaces is challenging as they are combinatorially large and gradients cannot be used in the same manner as in continuous spaces. We introduce map-elites with a Gradient-Informed Discrete Emitter (me-gide), which extends qd optimisation with differentiable functions over discrete search spaces. me-gide leverages the gradient information of the objective and descriptor functions with respect to its discrete inputs to propose gradient-informed updates that guide the search towards a diverse set of high quality solutions. We evaluate our method on challenging benchmarks including protein design and discrete latent space illumination and find that our method outperforms state-of-the-art qd algorithms in all benchmarks.
Self-adaptation Can Help Evolutionary Algorithms Track Dynamic Optima

Real-world optimisation problems often involve dynamics, where objective functions may change over time. Previous studies have shown that evolutionary algorithms (EAs) can solve dynamic optimisation problems. Additionally, the use of diversity mechanisms, populations, and parallelisation can enhance the performance of EAs in dynamic environments if appropriate parameter settings are utilised. Self-adaptation, which encodes parameters in genotypes of individuals and allows them to evolve together with solutions, can help configure parameters of EAs. This parameter control mechanism has been proved to effectively handle a static problem with unknown structure. However, the benefit of self-adaptation on dynamic optimisation problems remains unknown. We consider a tracking dynamic optima problem, the so-called Dynamic Substring Matching (DSM) problem, which requires algorithms to successively track a sequence of structure-changing optima. Our analyses show that mutation-based EAs with a fixed mutation rate have a negligible chance of tracking these dynamic optima, while the self-adaptive EA tracks them with an overwhelmingly high probability. Furthermore, we provide a level-based theorem with tail bounds, which bounds the chance of the algorithm finding the current optima within a given evaluation budget. Overall, self-adaptation is promising for tracking dynamic optima.
Lightweight Football Motion Recognition and Intensity Analysis Using Low-Cost Wearable Sensors

In recent years, machine learning has been utilized in health informatics and sports science. There is a great demand and development potential for combining the Internet of Things (IoT) and artificial intelligence (AI) to be applied to football sports. The conventional teaching and training methods of football sports have limited collection and mining of real raw data using wearable devices, and lack human motion capture and gesture recognition based on sports science theories. In this study, a low-cost AI + IoT system framework is designed to recognize football motion and analyze motion intensity. To reduce the communication delay and the computational resource consumption caused by data operations, a multitask learning model is designed to achieve motion recognition and intensity estimation. The model can perform classification and regression tasks in parallel and output the results simultaneously. A feature extraction scheme is designed in the initial data processing, and feature data augmentation is performed to solve the small sample data problem. To evaluate the performance of the designed football motion recognition algorithm, this paper proposes a data extraction experimental scheme to complete the data collection of different motions. Model validation is performed using three publicly available datasets, and the features learning strategies are analyzed. Finally, experiments are conducted on the collected football motion datasets and the experimental results show that the designed multitask model can perform two tasks simultaneously and can achieve high computational efficiency. The multitasking single-layer long short-term memory (LSTM) network with 32 neural units can achieve the accuracy of 0.8372, F1 score of 0.8172, mean average precision (mAP) of 0.7627, and mean absolute error (MAE) of 0.6117, while the multitasking single-layer LSTM network with 64 neural units can achieve the accuracy of 0.8407, F1 score of 0.8132, mAP of 0.7728, and MAE of 0.5966.
A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for Regression

Evolutionary feature construction is an important technique in the machine learning domain for enhancing learning performance. However, traditional genetic programming-based feature construction methods often suffer from bloat, which means the sizes of constructed features increase excessively without improved performance. To address this issue, this paper proposes a double-stage lexicase selection operator to control bloat while not damaging search effectiveness. This new operator contains a two-stage selection process, where the first stage selects individuals based on fitness values and the second stage selects individuals based on tree sizes. Therefore, the proposed operator can control bloat meanwhile leveraging the advantage of the lexicase selection operator. Experimental results on 98 regression datasets show that compared to the traditional bloat control method of having a depth limit, the proposed selection operator not only significantly reduces the sizes of constructed features on all datasets but also keeps a similar level of predictive performance. A comparative experiment with seven bloat control methods shows that the double lexicase selection operator achieves the best trade-off between the model performance and the model size.
MOAZ: A Multi-Objective AutoML-Zero Framework

Automated machine learning (AutoML) greatly eases human efforts in architecture engineering. However, mainstream AutoML methods like neural architecture search (NAS) are customized for well-designed search spaces wherein promising architectures are densely distributed. In contrast, AutoML-Zero builds machine-learning algorithms using basic primitives and can explore novel architectures beyond human knowledge. AutoML-Zero shows the potential to deploy machine learning systems by not taking advantage of either feature engineering or architectural engineering. In its current form, it only optimizes a single objective like accuracy and has no mechanism to ensure that the constraints of real-world applications are satisfied. We propose a multi-objective variant of AutoML-Zero called MOAZ, that distributes solutions on a Pareto front by trading off accuracy against the computational complexity of the machine learning algorithm. In addition to generating different Pareto-optimal solutions, MOAZ can effectively explore the sparse search space to improve search efficiency. Experimental results on linear regression tasks show MOAZ reduces the median complexity by 87.4% compared to AutoML-Zero while accelerating the median target performance achievement speed by 82%. In addition, our preliminary results on non-linear regression tasks show the potential for further improvements in search accuracy and for reducing the need for human intervention in AutoML.
Interactive neural cascade denoising for 1-spp Monte Carlo images

Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed.
Probabilistic model with evolutionary optimization for cognitive diagnosis

Cognitive Diagnostic Models (CDMs) aim to analyze students' cognitive levels of each knowledge component (KC) by mining educational data. Existing CDMs can be mainly divided into two categories, i.e., traditional probability-based and neural-network-based. Most probabilistic models have the advantages of simplicity and good interpretability, but suffer from slow training time in the case of a large number of KCs. Neural-network-based methods are widely considered to be superior to probabilistic models due to their good performance. However, neural network methods are less interpretable than probabilistic models, thus limiting their usefulness in practice. Because most existing probabilistic models are optimized iteratively based on single-point-based search methods, they may be easily trapped in local optimum due to the influence of the initial points. And evolutionary algorithms (EAs) have good global search ability. Therefore, an interesting question is whether a simple probabilistic model based on evolutionary optimization can rival neural-network in limited optimization time. Thus, a hybrid EA with a customized local search is proposed. Experimental results on three real-world datasets show that our method outperforms the compared 7 models (including 2 state-of-the-art neural-network-based models); and the running time of our method is significantly less than the compared probabilistic models.
Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems

Epsilon-lexicase selection is a parent selection method in genetic programming that has been successfully applied to symbolic regression problems. Recently, the combination of random subsampling with lexicase selection significantly improved performance in other genetic programming domains such as program synthesis. However, the influence of subsampling on the solution quality of real-world symbolic regression problems has not yet been studied. In this paper, we propose down-sampled epsilon-lexicase selection which combines epsilon-lexicase selection with random subsampling to improve the performance in the domain of symbolic regression. Therefore, we compare down-sampled epsilon-lexicase with traditional selection methods on common real-world symbolic regression problems and analyze its influence on the properties of the population over a genetic programming run. We find that the diversity is reduced by using down-sampled epsilon-lexicase selection compared to standard epsilon-lexicase selection. This comes along with high hyperselection rates we observe for down-sampled epsilon-lexicase selection. Further, we find that down-sampled epsilon-lexicase selection outperforms the traditional selection methods on all studied problems. Overall, with down-sampled epsilon-lexicase selection we observe an improvement of the solution quality of up to 85% in comparison to standard epsilon-lexicase selection.