Reviewerly/exHarmony huggingface dataset:
MNDE: Node-depth encoding can do better in evolutionary multitask algorithms

Evolutionary multitask algorithms that adopt multitask optimization paradigms have been proposed to tackle multiple problems simultaneously and improve the performance of traditional evolutionary algorithms. One of the most crucial challenges in evolutionary multitasking applied to network design is the lack of an efficient unified representation to encode solutions. This paper presents the first representation based on node-depth encoding for evolutionary multitask algorithms to tackle network design problems. Remarkably, we propose an encoding method to represent solutions modeled by trees of arbitrary graphs in the form of a unified representation and design a corresponding decoding method to reconstruct solutions from a unified search space for each task. To verify the efficiency of our proposed methods, extensive experiments are conducted on well-known network design problems and demonstrate that our approach performs significantly better than previous approaches regarding solution quality.
LCT-DER: Learning Classifier Table with Dynamic-Sized Experience Replay for Run-time SoC Performance-Power Optimization

Learning classifier tables (LCTs) are lightweight, classifier based, hardware implemented reinforcement learning (RL) building blocks which enable self-adaptivity and self-optimization properties in multicore systems. LCTs are deployed per-core to learn and optimize potentially conflicting objectives and constraints. Experience replay (ER) is a replay memory technique in RL, where agents experiences are stored in a buffer and are used to improve the learning process. Implementing an ER buffer in hardware requires memory and is expensive. We introduce LCT-DER: LCT with dynamic-sized experience replay, where the classifier population and experiences share the same memory by exploiting the concept of macro-classifiers. LCT-DER performing DVFS achieves 44.5% and 4.5% lower number of power budget overshoots and IPS difference compared to a standard LCT without requiring additional memory.
Towards Principled Synthetic Benchmarks for Explainable Rule Set Learning Algorithms

A very common and powerful step in the design process of a new learning algorithm or extensions and improvements of existing algorithms is the benchmarking of models produced by said algorithm. We propose a paradigm shift in the benchmarking of explainable if-then-rule-based models like the ones generated by Learning Classifier Systems or Fuzzy Rule-based Systems. The principled method we suggest is based on synthetic data sets being sampled from randomly generated but known processes that have the same overall structure as the models that are being benchmarked (i. e. each process consists of a set of components each of which corresponds to one if-then rule) which is up-to-date not available among the many synthetic data generators. This approach has several benefits over other benchmarks and we expect that it will lead to fresh insights into how algorithms producing such explainable models work and can be improved. We demonstrate its usage by benchmarking the effects of different rule representations in the XCSF classifier system.
Estimation of Interstitial Glucose from physical activity measures using Grammatical Evolution

People with diabetes need to have their glucose levels under control, and it is essential for them to be able to know or estimate their glucose levels at any time. Continuous glucose monitors are commonly used, which measure interstitial glucose, an approximation of blood glucose, by means of a small catheter. Although most devices are not very intrusive, they do present some discomfort, and it would be preferable if these glucose levels could be estimated non-invasively, for example, through other physiological measurements collected in a simple way. This abstract describes our research on the performance of different grammatical evolution techniques to obtain accurate estimations of actual subcutaneous glucose values from non-invasive physiological measures, steps, calories and heart rates obtained with commercial smartwatches.
Stability, Entropy and Performance in PSO

A main focus in the theory of Particle Swarm Optimization (PSO) was the convergence of the particles where various frameworks and assumptions have been adopted which lead to a variety of outcomes differing in their relevance as a predictor of the performance of the algorithm. We discuss here the interesting phenomenon that the study of the stochastic dynamics of the particles provides a limit case, while the average dynamics yields a different limit. We can specify differences between the best particle and other particles in the swarm which has an effect on the performance of the algorithm and was not considered in previous approaches. In addition to benchmark results, also the swarm entropy confirms this formulation of the stability properties of PSO. This framework enables us to describe the time scales of excursions of particles away from the best location found so far, which indicates the diversity of locations in the search space discovered so far by the particles.
A Comparison of Structural Complexity Metrics for Explainable Genetic Programming

Genetic Programming (GP) has the potential to generate intrinsically explainable models. Despite that, in practice, this potential is not fully achieved because the solutions usually grow too much during the evolution. The excessive growth together with the functional and structural complexity of the solutions increase the computational cost and the risk of overfitting. Thus, many approaches have been developed to prevent the solutions to grow excessively in GP. However, it is still an open question how these approaches can be used for improving the interpretability of the models. This article presents an empirical study of eight structural complexity metrics that have been used as evaluation criteria in multi-objective optimisation. Tree depth, size, visitation length, number of unique features, a proxy for human interpretability, number of operators, number of non-linear operators and number of consecutive nonlinear operators were tested. The results show that potentially the best approach for generating good interpretable GP models is to use the combination of more than one structural complexity metric.
Adaptive Landscape-aware Constraint Handling with Application to Binary Knapsack Problem

Real-world optimization problems frequently have constraints that define feasible and infeasible combinations of decision variables. Evolutionary algorithms do not inherently work with constraints, so they must be modified to include a suitable constraint handling technique (CHT) before they can be used to solve such problems. A range of different approaches to handling constraints have been used effectively with evolutionary algorithms, such as penalty-based, repair, feasibility rules, and bi-objective approaches. In this study we investigate different CHTs with an evolutionary algorithm on the 0/1 knapsack problem. We present results to show that performance complementarity exists between different CHTs on the knapsack problem. Landscape analysis is then used to characterize the search space of a large number of knapsack instances, and decision tree induction is used to derive rules for selecting the most appropriate CHT and switching it adaptively based on landscape features. We finally implement a landscape-aware CHT approach and show that it out-performs the constituent CHT approaches.
Eco-evolutionary Dynamics of Non-episodic Neuroevolution in Large Multi-agent Environments

Neuroevolution (NE) has recently proven a competitive alternative to learning by gradient descent in reinforcement learning tasks. However, the majority of NE methods and associated simulation environments differ crucially from biological evolution: the environment is reset to initial conditions at the end of each generation, whereas natural environments are continuously modified by their inhabitants; agents reproduce based on their ability to maximize rewards within a population, while biological organisms reproduce and die based on internal physiological variables that depend on their resource consumption; simulation environments are primarily single-agent while the biological world is inherently multi-agent and evolves alongside the population. In this work we present a method for continuously evolving adaptive agents without any environment or population reset. The environment is a large grid world with complex spatiotemporal resource generation, containing many agents that are each controlled by an evolvable recurrent neural network and locally reproduce based on their internal physiology. The entire system is implemented in JAX, allowing very fast simulation on a GPU. We show that NE can operate in an ecologically-valid non-episodic multi-agent setting, finding sustainable collective foraging strategies in the presence of a complex interplay between ecological and evolutionary dynamics.
Iterative Structure-Based Genetic Programming for Neural Architecture Search

In this paper we present an iterative structure-based genetic programming algorithm for neural architecture search. Canonical genetic programming uses a fitness function to determine where to move the search to in the program space. This research investigates using the structure of the syntax trees, representing different areas of the program space, in addition to the fitness function to direct the search. The structure is used to avoid areas of the search that previously led to local optima both globally (exploration) and locally (exploitation). The proposed approach is evaluated for image classification and video shorts creation. The iterative structure-based approach was found to produce better results then canonical genetic programming for both problem domains, with a slight reduction in computational cost. The approach also produced better results than genetic algorithms which are traditionally used for neural architecture search.
ARD-Stream: An adaptive radius density-based stream clustering

With the proliferation of applications generating vast volumes of data streams, numerous clustering methods have emerged to process and extract valuable insights from this data. These methods typically involve online and offline phases. During the online phase, data summaries are stored in micro-clusters, which serve as compact representations of the data. In the subsequent offline phase, static clustering techniques are applied to these micro-clusters to derive the final clusters. However, these methods often employ fixed parameters for creating micro-clusters in the online phase, which can result in the loss of data due to evolving behavioral patterns in the data stream over time. In this study, we propose a novel approach to address this limitation. We introduce a dynamic radius threshold for each micro-cluster in the online phase, allowing for fine adaptation to statistical changes in the data stream distribution. Furthermore, we present a novel method for generating the final clusters in the offline phase. By considering both the shared density and distance between micro-clusters, we overcome the challenge of neglecting density relationships in previous approaches, leading to more accurate clusters. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on synthetic, real-world benchmark, and Twitter datasets. The results demonstrate that our approach outperforms state-of-the-art methods in accurately identifying the correct clusters amidst the dynamic nature of the data streams.
The Problem Solving Benefits of Down-sampling Vary by Selection Scheme

Genetic programming systems often use large training sets to evaluate candidate solutions, which can be computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. Indeed, recent studies have shown that both random and informed down-sampling can substantially improve problem-solving success for GP systems that use lexicase parent selection. We use the PushGP framework to experimentally test whether these down-sampling techniques can also improve problem-solving success in the context of two other commonly used selection methods, fitness-proportionate and tournament selection, across eight GP problems (four program synthesis and four symbolic regression). We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection. However, the number of problems wherein down-sampling improved problem-solving success varied by selection scheme, suggesting that the impact of down-sampling depends both on the problem and choice of selection scheme. Surprisingly, we found that down-sampling was most consistently beneficial when combined with lexicase selection as compared to tournament and fitness-proportionate selection. Overall, our results suggest that down-sampling should be considered more often when solving test-based GP problems.
Comparing Solution Combination Techniques in Scatter Search for Quadratic Unconstrained Binary Optimization

Quadratic Unconstrained Binary Optimization (QUBO) has emerged as a vital unifying model for combinatorial optimization problems, and (meta-)heuristic approaches are commonly used to solve them due to their NP-hard nature. Scatter Search (SS), a population-based metaheuristic framework, is one such method that has shown promising results for QUBO problems. Generating new solutions from more promising ones is a crucial operation in SS. Path Relinking (PR) based SS has been previously used to solve challenging QUBO problems with high-quality solutions. This paper introduces two new variants of the SS algorithm. The first is the (Multi) Uniform Crossover (MUC) based SS while the second is the Univariate Marginal Distribution Algorithm (UMDA) based SS. MUC and UMDA are well-known operators in Genetic Algorithms and Estimation of Distribution Algorithms respectively. When compared to the existing PR based SS, this work shows that more promising results can be achieved when the newly proposed MUC and UMDA-based SS are applied to QUBO formulations of the Quadratic Knapsack Problem (QKP) instances.
Optimizing Keyboard Configuration Using Single and Multi-Objective Evolutionary Algorithms

An English language keyboard configuration involves 30 keys - 26 alphabets and four punctuation marks: comma, period, semi-colon, and question mark. Based on the sequence of letters that frequently appear in typing activities, one or more keyboard configurations will be optimal for a specific performance metric. Here, we consider the cumulative distance between two consecutive keystrokes to type out a large piece of text as the optimization objective, as it directly relates to the typing efficiency. The shorter the distance the fingers needs to move between strokes, the faster is the typing speed. When considering such a scenario, researchers often focus on a specific single objective one at a time and come up with the respective optimal keyboard configuration. The complete dissimilarity of these optimal, yet esoteric, keyboards from the current in-practice QWERTY configuration makes the new optimal keyboard configuration harder to learn and deploy on a larger scale. Hence in this study, we look at a multi-objective version of such a problem and consider two objectives - maximizing typing efficiency and maximizing similarity to QWERTY configuration. We make use of the NSGA-II algorithm and produce a Pareto set of keyboard configurations ranging from the most efficient to the most similar configurations. The range of solutions can potentially provide a design innovation path for gradually moving from the popular QWERTY configuration to more efficient configurations as a series of improvements.
Explaining Memristive Reservoir Computing Through Evolving Feature Attribution

Memristive Reservoir Computing (MRC) is a promising computing architecture for time series tasks, but lacks explainability, leading to unreliable predictions. To address this issue, we propose an evolutionary framework to explain the time series predictions of MRC systems. Our proposed approach attributes the feature importance of the time series via an evolutionary approach to explain the predictions. Our experiments show that our approach successfully identified the most influential factors, demonstrating the effectiveness of our design and its superiority in terms of explanation compared to state-of-the-art methods.
Challenges and Opportunities in the Remote Sensing Big Data

This chapter discusses challenges and opportunities in remote sensing big data. Three challenges are discussed. They are data complexity, data quality, and infrastructure change. The growth of remote sensing big data also introduces several new opportunities. The discussed changes are single scale to multiscale, on-premise servers to distributed services, data-focused Sensor Web to modeled-simulation Digital Twins, “meteorology” snapshots to “climate” series, isolated case to intertwined “teleconnections,” and one-level system to hierarchical knowledge graph.
Design of a new four-dimensional chaotic system and its application to color image encryption

In this paper, a new four-dimensional chaotic system is constructed. Then, the dynamics of the phase diagram, Lyapunov exponent, bifurcation, and complexity of the system are investigated, and the simulation circuit of the system is designed and the trajectory simulation of the system is implemented using a field programmable gate array. National Institute of Standards and Technology tests have verified the randomness of the output of the chaotic sequence by the system. Finally, a new color image encryption algorithm is designed based on this system. In the proposed algorithm, the plaintext information is used to generate the initial value of the system through a hashing algorithm, and the plaintext information is applied to the algorithm, reflecting the idea of algorithmic adaption. The bit-plane decomposition row dislocation breaks the strong correlation between pixels, and finally, the DNA encoding is combined with the diffusion method to complete the complete encryption process. Multiple security analysis methods show that the scheme has a good encryption effect.
Development of Smart Chabot in the Field of Trading using Smart Artificial Intelligence Informal Technology

Artificial Intelligence (AI) and Natural Language Processing (NLP) calculations are being used to create a sophisticated system known as a chatbot. It answers questions from customers and successfully communicates with them. Discourse/discussion administrators are most frequently used by organizations, official affiliations, and fictitious affiliations. Organizations that deal with money, like banks, charge card companies, associations with online retailers, and new businesses, frequently disseminate them. These conversational specialists work for a variety of companies, from huge associations to tiny, new businesses. There are chatbot advancement frameworks on the market that are both interface-and code-based. They do their best to avoid being nimble and adaptable when starting sincere conversations though. There are several well-known intelligent personal assistants, including "Amazon's Alexa, Google's Google Assistant, and Microsoft's Cortana." These experts only have a few components. The current chatbots don't deliver satisfactory results, regardless of whether they were developed using recovery-based strategies, rule-based tactics, or basic AI computations. This article offers a basic audit of chatbots and carefully examines and discusses the available strategies.
Node representation learning with graph augmentation for sequential recommendation

Current sequential recommender systems primarily focus on modeling users' dynamic interests over time by leveraging GNN-based approaches. However, most studies tend to overlook the impact of high-order data outside the sequence and its corresponding position information on users' interest, especially fail to maximize the collaborative signals between users or items. To alleviate the aforementioned problems, we propose a novel sequential recommendation model, called GNSR, which utilizes graph structures at various levels to enhance the node representation and ultimately improve recommendation performance. Specifically, we construct a high-dimensional continuous-time bipartite graph centered on target users and their historical sequences while preserving the interaction order. Subsequently, we create meta-path graphs through meta-path exploration. Additionally, to capture both effective high-dimensional collaborative signals and dynamic changes in users' interests throughout the sequence, we design a High-Dimensional Information Collaboration Module within GNSR, which enables a comprehensive understanding of user behavior. Finally, we conduct extensive experiments on three public datasets and demonstrate that the performance of GNSR improves by approximately 0.1% ∼ 3.2% over selected baselines.
An ablation study on part-based face analysis using a Multi-input Convolutional Neural Network and Semantic Segmentation

Face-based recognition methods usually need the image of the whole face to perform, but in some situations, only a fraction of the face is visible, for example wearing sunglasses or recently with the COVID pandemic we had to wear facial masks. In this work, we propose a network architecture made up of four deep learning streams that process each one a different face element, namely: mouth, nose, eyes, and eyebrows, followed by a feature merge layer. Therefore, the face is segmented into the part of interest by means of ROI masks to keep the same input size for the four network streams. The aim is to assess the capacity of different combinations of face elements in recognizing the subject. The experiments were carried out on the Masked Face Recognition Database (M2FRED) which includes videos of 46 participants. The obtained results are 96% of recognition accuracy considering the four face elements; and 92%, 87%, and 63% of accuracy for the best combination of three, two, and one face elements respectively.
Deep learning-based text knowledge classification for whole-process engineering consulting standards

The knowledge classification technology has significant implications for the intelligent research of industries. In the field of whole-process engineering consulting, manually reading and processing large amounts of text data is both time-consuming and laborious. Knowledge classification technology can automatically classify these text data and extract key information, which can improve industry work efficiency. In this study, a deep learning-based text knowledge classification method is proposed to address the large-scale text classification problem in the whole-process engineering consulting field. Firstly, pre-trained language models such as RoBERTa, BERT, and Longformer-RoBERTa are used to extract features from text. Secondly, a multi-label classification model is used to classify the text. Experimental results show that the proposed method performs better than other commonly used models in both overall classification performance and individual category classification performance. Moreover, when the text knowledge classification model is integrated as a text representation module with common classification models such as CNN and LSTM, its performance is inferior to that of a pure classification model. The proposed text knowledge classification method is of great significance for the application in the field of whole-process engineering consulting and provides an effective solution for intelligent research in engineering consulting.