Reviewerly/exHarmony huggingface dataset:
Evaluation of draping simulations by means of 3D laser scans and robot supported fiber angle scans

In order to demonstrate the accuracy of macroscopic finite element draping simulations it is necessary to compare the results with experiments. In this work, a compact overview of evaluation methods for draping simulations based on experiments, in the recent literature, is provided. Then, a method using state of the art 3D laser scans (RS6, Hexagon) and robot supported fiber angle scans (FScan, Profactor) is described. The presented comparison of draping results with the tool geometry in 3D allows for an evaluation of wrinkles and bridging areas. For the evaluation of the edge contour, the commonly used method of projecting the edge contour on a 2D plane is extended to a comparison in 3D space. To determine fiber orientations and compare them with the predictions from simulations, a robot supported fiber angle sensor and a script-based mapping and comparison algorithm are used. The results are further analyzed statistically, to derive comparative figures to other results found in the literature. The location and dimensions of macroscopic manufacturing effects such as fiber bridging and wrinkles could be predicted accurately. The final component contour could be on average predicted within 5.2 mm. The fiber orientation could be predicted with a deviation of less than 2° for approx. 65% and within 6° for 95% of the part surface for UD laminas. Cross-ply laminas showed larger deviations, only 40% of the part surface was within 2° and 80% within 6°, compared to the experiment. Overall, the results for the presented methods show good agreement between multi-ply draping experiments and macroscopic simulations conducted with the Abaqus Fabric material model.
Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise

Evolutionary algorithms are known to be robust to noise in the evaluation of the fitness. In particular, larger offspring population sizes often lead to strong robustness. We analyze to what extent the (1 + (Λ, Λ)) genetic algorithm is robust to noise. This algorithm also works with larger offspring population sizes, but an intermediate selection step and a non-standard use of crossover as repair mechanism could render this algorithm less robust than, e.g., the simple (1 + Λ) evolutionary algorithm. Our experimental analysis on several classic benchmark problems shows that this difficulty does not arise. Surprisingly, in many situations this algorithm is even more robust to noise than the (1 + Λ) EA.
Co-operative Co-evolutionary Many-objective Embedded Multi-label Feature Selection with Decomposition-based PSO

Multi-label classification is an emerging machine-learning problem involving the prediction of a set of class labels based on the instance's features. In real-world problems, there are hundreds or thousands of features, many of which are irrelevant or redundant, resulting in a large search space for feature selection, i.e. to find small and discriminate feature subsets that improve the classification performance. Feature selection for multi-label classification is a many-objective optimisation problem with more than three main conflicting objectives: one of which is to reduce the number of selected features. There are many metrics for measuring multi-label classification performance, each of which can conflict with one another depending on the task. Hence, multi-label feature selection is a many-objective optimisation problem when three or more classification metrics and the number of selected features are optimised. In this paper, we propose to combine multi-label feature selection with evolutionary many-objective optimisation to address the above challenges and handle the trade-offs between multiple classification metrics and the number of selected features, using a decomposition-based algorithm. The results demonstrate that our proposed method is capable of finding discriminative and small feature subsets that can significantly improve the classification performances in comparison with other many-objective feature selection approaches.
Quality-diversity in dissimilarity spaces

The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-diversity algorithms in generic dissimilarity spaces. In particular, we instantiate and demonstrate a very general version of Go-Explore with promising performance.
Interactively learning behavior trees from imperfect human demonstrations

Introduction: In Interactive Task Learning (ITL), an agent learns a new task through natural interaction with a human instructor. Behavior Trees (BTs) offer a reactive, modular, and interpretable way of encoding task descriptions but have not yet been applied a lot in robotic ITL settings. Most existing approaches that learn a BT from human demonstrations require the user to specify each action step-by-step or do not allow for adapting a learned BT without the need to repeat the entire teaching process from scratch. Method: We propose a new framework to directly learn a BT from only a few human task demonstrations recorded as RGB-D video streams. We automatically extract continuous pre- and post-conditions for BT action nodes from visual features and use a Backchaining approach to build a reactive BT. In a user study on how non-experts provide and vary demonstrations, we identify three common failure cases of an BT learned from potentially imperfect initial human demonstrations. We offer a way to interactively resolve these failure cases by refining the existing BT through interaction with a user over a web-interface. Specifically, failure cases or unknown states are detected automatically during the execution of a learned BT and the initial BT is adjusted or extended according to the provided user input. Evaluation and results: We evaluate our approach on a robotic trash disposal task with 20 human participants and demonstrate that our method is capable of learning reactive BTs from only a few human demonstrations and interactively resolving possible failure cases at runtime.
To slide or not to slide? Moving along fitness levels and preserving the gene subsets diversity in modern evolutionary computation

Optimal Mixing (OM) is a mating operator employed by many state-of-the-art Genetic Algorithms (GAs). This paper identifies the sliding phenomenon defined as a serie of the so-called plateau moves. Sliding is a part of the original OM proposition. Although sliding seems to be a minor element of OM, we show that performing or avoiding it may significantly affect the effectiveness of OM-employing GAs. Therefore, we analyze the details of sliding pros and cons and propose the Autonomous Slide Deciding Algorithm (ASDA). ASDA analyzes the diversity of the population for a given subset of genes. Then, it decides, for a given mixing operation, sliding will be profitable or not. We show that using ASDA is greedily beneficial for two different state-of-the-art GAs. Additionally, we explain why ASDA deteriorates the effectiveness of the third considered OM-using GA.
An Investigation of Geometric Semantic GP with Linear Scaling

Geometric semantic genetic programming (GSGP) and linear scaling (LS) have both, independently, shown the ability to outperform standard genetic programming (GP) for symbolic regression. GSGP uses geometric semantic genetic operators, different from the standard ones, without altering the fitness, while LS modifies the fitness without altering the genetic operators. So far, these two methods have already been joined together in only one practical application. However, to the best of our knowledge, a methodological study on the pros and cons of integrating these two methods has never been performed. In this paper, we present a study of GSGP-LS, a system that integrates GSGP and LS. The results, obtained on five hand-tailored benchmarks and six real-life problems, indicate that GSGP-LS outperforms GSGP in the majority of the cases, confirming the expected benefit of this integration. However, for some particularly hard datasets, GSGP-LS overfits training data, being outperformed by GSGP on unseen data. Additional experiments using standard GP, with and without LS, confirm this trend also when standard crossover and mutation are employed. This contradicts the idea that LS is always beneficial for GP, warning the practitioners about its risk of overfitting in some specific cases.
Hybrid BBM92 approach for GEOQKD: lab implementation and future perspectives

Quantum Key Distribution (QKD) is the most mature quantum technology, having achieved on-ground applications and commercially available products.In this domain, satellite platforms are essential to achieve a communication range reaching the intercontinental scale, acting thus as enablers for the future global quantum internet operation.This paper will report on the main results of the TNO-Eutelsat-TAS Italy project aiming to evaluate a novel hybrid approach for improved QKD performance particularly suited for geostationary orbits (GEO); the Dutch TKI HTSM sponsors the overall activity.We will present the results of lab-based tests of a novel QKD free-space approach that simultaneously implements the BBM92 protocol in both trusted and trust-free mode, following a joint Eutelsat-TNO patent.The trust-free mode between two ground receivers is the standard BBM92 protocol that uses entangled photons and there is no need for further security assumptions on the satellite payload.In trusted mode operation, one of the two entangled photons is measured directly on board.Key material is generated between ground and satellite.Security measures will be needed in the space segment, which therefore needs to be trusted.Further, we will present a demonstration roadmap aiming at free space field test to validate loss and key rate models for a free space link up to 2.5km in one of the arms.We also present a perspective on potential future GEO-based quantum applications beyond QKD.
Leveraging Fitness Critics To Learn Robust Teamwork

Co-evolutionary algorithms have successfully trained agent teams for tasks such as autonomous exploration or robot soccer. However generally, such approaches seek a single strong team, whereas many real-world applications require agents to effectively cooperate across multiple teams. To adapt to different teammates, agents need to learn more general teamwork skills rather than a single team-specific role. Previous work primarily frames this as a fitness-shaping problem, providing high-quality but expensive evaluation methods to isolate an agent's contribution. In this work, we introduce Learned Evaluations for Robust Teaming (LERT), an approach that provides a local evaluation that leverages state trajectories of agents to better quantify their impact across multiple teams. The key insight of this work is that agent state trajectories and previous experiences carry sufficient information to map agent abilities to team performance. As a result, LERT cooperatively co-evolves agents to work together across arbitrary teams. While only using local information and significantly fewer team evaluations, LERT performs as well as-if not better than-current methods.
Evolutionary Approach to Recommender Systems Improvement by Directory of Products Optimization

This paper concerns improving recommender systems using evolutionary algorithms that optimize the composition of the directory of products in the recommender system. It focuses on merging some infrequent products into certain groups of similar products and replacing the regular products by some aggregates of products, such as a category of products or a cluster of products in the product embedding space, and preparing recommendations with such aggregates. Evolutionary algorithms endeavor to decide which products should be processed as single products and which ones should be merged with others into smaller or larger groups of products (merging products into groups is done by a given auxiliary merging strategy that cluster similar products into groups) with the aim to maximize the accuracy of the recommender system. Computational experiments on benchmark datasets confirmed the efficiency of the approach.
Runtime Analysis with Variable Cost

The usual approach in runtime analysis is to derive estimates on the number of fitness function evaluations required by a method until a suitable element of the search space is found. One justification for this is that in real applications, fitness evaluation often contributes the most computational effort. A tacit assumption in this approach is that this effort is uniform and static across the search space. However, this assumption often does not hold in practice: some candidates may be far more expensive to evaluate than others. This might occur, for example, when fitness evaluation requires running a simulation or training a machine learning model.
Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space

Generating new instances via evolutionary methods is commonly used to create new benchmarking data-sets, with a focus on attempting to cover an instance-space as completely as possible. Recent approaches have exploited Quality-Diversity methods to evolve sets of instances that are both diverse and discriminatory with respect to a portfolio of solvers, but these methods can be challenging when attempting to find diversity in a high-dimensional feature-space. We address this issue by training a model based on Principal Component Analysis on existing instances to create a low-dimension projection of the high-dimension feature-vectors, and then apply Novelty Search directly in the new low-dimension space. We conduct experiments to evolve diverse and discriminatory instances of Knapsack Problems, comparing the use of Novelty Search in the original feature-space to using Novelty Search in a low-dimensional projection, and repeat over a given set of dimensions. We find that the methods are complementary: if treated as an ensemble, they collectively provide increased coverage of the space. Specifically, searching for novelty in a low-dimension space contributes 56% of the filled regions of the space, while searching directly in the feature-space covers the remaining 44%.
Mirror Descent of Hopfield Model

Mirror descent is an elegant optimization technique that leverages a dual space of parametric models to perform gradient descent. While originally developed for convex optimization, it has increasingly been applied in the field of machine learning. In this study, we propose a novel approach for using mirror descent to initialize the parameters of neural networks. Specifically, we demonstrate that by using the Hopfield model as a prototype for neural networks, mirror descent can effectively train the model with significantly improved performance compared to traditional gradient descent methods that rely on random parameter initialization. Our findings highlight the potential of mirror descent as a promising initialization technique for enhancing the optimization of machine learning models.
Symbolic Regression Trees as Embedded Representations

Representation learning is an area responsible for learning data representations that makes it easier for machine learning algorithms to extract useful information from them. Deep learning currently has the most effective methods for this task and can learn distributed representations - also known as embeddings - able to represent different properties of the data and their relationship. In this direction, this paper introduces a new way to look at tree-like GP individuals for symbolic regression. Given a set of predefined operators and a sufficiently large number of solutions sampled from the space, we train a transformer to learn an encoding/decoding function. By transforming a tree representation into a distributed representation, we are able to measure distances between trees in a much more efficient way and, more importantly, generate the potential for these representations to capture semantics. We show the distance accounting for embedding presents results very similar to those of a tree-edition, which reflects their syntactic similarity. Although the model as it stands is not able to capture semantics yet, we show its potential by using the generated tree-representation model in a simple task: measuring distances between trees in a fitness-sharing scenario.
The Impact of Asynchrony on Parallel Model-Based EAs

In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the algorithm and wastes computational resources. Alternatively, an EA can be made asynchronous parallel. However, EAs using classic recombination and selection operators (GAs) are known to suffer from an evaluation time bias, which also influences the performance of the approach. Model-Based Evolutionary Algorithms (MBEAs) are more scalable than classic GAs by virtue of capturing the structure of a problem in a model. If this model is learned through linkage learning based on the population, the learned model may also capture biases. Thus, if an asynchronous parallel MBEA is also affected by an evaluation time bias, this could result in learned models to be less suited to solving the problem, reducing performance. Therefore, in this work, we study the impact and presence of evaluation time biases on MBEAs in an asynchronous parallelization setting, and compare this to the biases in GAs. We find that a modern MBEA, GOMEA, is unaffected by evaluation time biases, while the more classical MBEA, ECGA, is affected, much like GAs are.
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems

The domain of an optimization problem is seen as one of its most important characteristics. In particular, the distinction between continuous and discrete optimization is rather impactful. Based on this, the optimizing algorithm, analyzing method, and more are specified. However, in practice, no problem is ever truly continuous. Whether this is caused by computing limits or more tangible properties of the problem, most variables have a finite resolution.
Learning-Based Neural Ant Colony Optimization

In this paper, we propose a new ant colony optimization algorithm, called learning-based neural ant colony optimization (LN-ACO), which incorporates an "intelligent ant". This intelligent ant contains a convolutional neural network pre-trained on a large set of instances which is able to predict the selection probabilities of the set of possible choices at each step of the algorithm. The intelligent ant is capable of generating a solution based on knowledge learned during training, but also guides other 'traditional' ants in improving their choices during the search. As the search progresses, the intelligent ant is also influenced by the pheromones accumulated by the colony, leading to better solutions. The key idea is that if tasks or instances share common features either in terms of their search landscape or solutions, then information learned by solving one instance can be applied to substantially accelerate the search on another. We evaluate the proposed algorithm on two public datasets and one real-world test set in the path planning domain. The results demonstrate that LN-ACO is competitive in its search capability compared to other ACO methods, with a significant improvement in convergence speed.
Fast and Efficient Local-Search for Genetic Programming Based Loss Function Learning

In this paper, we develop upon the topic of loss function learning, an emergent meta-learning paradigm that aims to learn loss functions that significantly improve the performance of the models trained under them. Specifically, we propose a new meta-learning framework for task and model-agnostic loss function learning via a hybrid search approach. The framework first uses genetic programming to find a set of symbolic loss functions. Second, the set of learned loss functions is subsequently parameterized and optimized via unrolled differentiation. The versatility and performance of the proposed framework are empirically validated on a diverse set of supervised learning tasks. Results show that the learned loss functions bring improved convergence, sample efficiency, and inference performance on tabulated, computer vision, and natural language processing problems, using a variety of task-specific neural network architectures.
Modular Differential Evolution

New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as extensions of a preexisting algorithm. Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants. This happens because even small changes in the experimental setup, parameter settings, or implementation details can cause results to become incomparable. Modular algorithms offer a way to overcome these challenges. By implementing the algorithmic modifications into a common framework, many algorithm variants can be compared, while ensuring that implementation details match in all versions.
Beat Box

Music Applications are connected to Human emotions that are not consistent and they are actually result of circumstances happening in individuals. Music is one of the best ways to relieve pressure in stressful modern society life. It unites us across markets, ages, backgrounds, languages, preference[1]. Youths are so obsessed that not only day chores even during rest time they prefer music. Music Application have a high demand as this can be used anytime, anywhere, and can be combined with daily activities, travelling, sports. With the rapid development of mobile network and digital music technologies, digital music has become the mainstream consumer content sought by many young people. Nowadays, the internet is growing fast and a large group of people has access to the internet, but people always prefer songs of offline.
2022 ECMWF-ESA workshop report: current status, progress and opportunities in machine learning for Earth System observation and prediction

This report provides a summary of the main outcomes of the 3rd edition of the workshop on Machine Learning (ML) for Earth System Observation and Prediction (ESOP/ML4ESOP) co-organised by the European Centre for Medium-Range Weather Forecasts (ECMWF) and European Space Agency (ESA). The 4-day workshop was held on 14-17 November 2022 in hybrid format, with an in-person component at the ECMWF Reading site and an interactive online component, attracting a record number of submissions and over 700 registrations. The workshop aimed to document the current state-of-the-art, progress and challenges in the rapidly evolving field of the integration of ML technologies in ESOP and to provide a venue for discussion and collaboration for ESOP and ML specialists. The workshop was structured along five main thematic areas covering the principal components of standard ESOP workflows. Highlights from the presentations and a discussion of the most promising development directions from the workshop Working Groups in all the different thematic areas are provided in this Report.