Reviewerly/exHarmony huggingface dataset:
Exploratory Landscape Analysis

tutorial Free Access Share on Exploratory Landscape Analysis Authors: Pascal Kerschke "Friedrich List" Faculty of Transport and Traffic Sciences, TU Dresden, Dresden, Germany Center for Scalable Data Science (ScaDS.AI), Dresden/Leipzig, Germany "Friedrich List" Faculty of Transport and Traffic Sciences, TU Dresden, Dresden, Germany Center for Scalable Data Science (ScaDS.AI), Dresden/Leipzig, Germany https://orcid.org/0000-0003-2862-1418Search about this author , Mike Preuss LIACS, University of Leiden, Leiden, Netherlands LIACS, University of Leiden, Leiden, Netherlands https://orcid.org/0000-0003-4681-1346Search about this author Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 990–1007https://doi.org/10.1145/3583133.3595058Published:24 July 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation

Co-evolutionary algorithms have found several applications in game-theoretic applications and optimisation problems with an adversary, particularly where the strategy space is discrete and exponentially large, and where classical game-theoretic methods fail. However, the application of co-evolutionary algorithms is difficult because they often display pathological behaviour, such as cyclic behaviour and evolutionary forgetting. These challenges have prevented the broad application of co-evolutionary algorithms.
Interactive Evolutionary Computation for Strategy Discovery in Multi-Phase Operations

Complex adversarial operations typically involve the allocation of finite resources to meet a set of objectives over a number of phases. This poses a challenge for AI-based strategy discovery. A strategy for one phase cannot be developed in isolation as the resources available in any one phase are dependent on the outcome of previous phases. Our proposed solution is to combine an evolutionary algorithm search with human-guided evaluation. The approach uses simulation-based fitness evaluation, where a human operator can view the fittest solution after every set number of generations. The operator can 'lock in' strategies for particular phases, and 'suggest' alternative strategies to guide further evolution. Key to our approach is a representation encoding that allows relative proportions of resources to be represented where actual levels may not be known a priori. We evaluate our solution on a three-phase scenario of a real-time strategy game and compare the effectiveness of strategies that were purely human-devised, purely evolved, and those resulting from the human-evolution collaboration. The collaborative approach shows promising results in being able to find an optimum solution earlier.
Simultaneous Evolutionary Optimization of Features Subset and Clusters Number

Cluster analysis is a popular technique used to identify patterns in data mining. However, evaluating the accuracy of a clustering task is a challenging process which remains to be an open issue. In this work, we focus on two factors that significantly influence clustering performance: the optimal number of clusters and the subset of relevant attributes. While the former has been extensively studied, the latter has received comparatively less attention, especially in relation to its equivalent in supervised learning. Despite their clear interdependence, these factors have rarely been studied together. In this context, we propose an evolutionary algorithm that simultaneously optimizes both factors using ad-hoc variations of internal validation indices as a fitness function.
Transfer Learning in Evolutionary Spaces

tutorial Open Access Share on Transfer Learning in Evolutionary Spaces Author: Nelishia Pillay Department of Computer Science, University of Pretoria, Pretoria, South Africa, Pretoria, South Africa Department of Computer Science, University of Pretoria, Pretoria, South Africa, Pretoria, South Africa https://orcid.org/0000-0003-3902-5582View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1037–1047https://doi.org/10.1145/3583133.3595038Published:24 July 2023Publication History 0citation20DownloadsMetricsTotal Citations0Total Downloads20Last 12 Months20Last 6 weeks14 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Sparse Mutation Decompositions: Fine Tuning Deep Neural Networks with Subspace Evolution

Neuroevolution is a promising area of research that combines evolutionary algorithms and neural networks. A popular subclass of neuroevolutionary methods, called evolution strategies, rely on dense noise perturbations to mutate networks, which can be sample inefficient and challenging for large models with millions of parameters. We introduce an approach to alleviating this problem by decomposing dense mutations into low-dimensional subspaces. Restricting mutations in this way can significantly reduce variance as networks can handle stronger perturbations while maintaining performance. This approach is uniquely effective for the task of fine tuning pre-trained models, which is an increasingly valuable area of research as networks continue to scale in size and open source models become more widely available. We conduct an exploration of sparse mutation decompositions on the difficult ImageNet dataset, where we see small generalization improvements with only a single evolutionary generation across a wide variety of deep neural network architectures.
Adaptive Particle Swarm Optimization with Local Search for Multi-robot Multi-point Dynamic Aggregation

Multi-point dynamic aggregation (MPDA) problem is a representative model to describe the task allocation in a multi-robot system. It is a NP-hard problem in which several robots cooperate to finish a set of distributed tasks. Considering the strong exploration ability and simple structure of particle swarm optimization (PSO), in this paper, we propose a new algorithm, called adaptive PSO with local search to solve MPDA effectively and efficiently. Since MPDA is a constraint problem with huge and complex search space, an adaptive initialization method is designed to generate feasible seed solutions guiding the swarm to feasible regions quickly, especially for the situations where robots need to cooperate closely. Meanwhile, an adaptive parameter is set to control the execution of initialization according to the characteristics of the problem. Then, to further reduce the search space, the velocity update rule is modified based on the random keys representation to limit the searching range of particles. Finally, a new local search strategy considering the cooperation relationship between robots is introduced to enhance the exploitation ability of the algorithm. The experimental results show that the proposed algorithm is more effective and efficient than the state-of-the-art methods.
A Preliminary Framework and Empirical Studies on Leveraging the Use of Persuasion in Product Design

The paper proposes a novel framework for understanding and implementing persuasive design tools during the design process. The framework seeks to help designers use persuasion in designing products and services by incorporating persuasive tools and behavioral triggers. While there are multiple theoretical approaches, models, and tools for persuasion and related factors, there is inadequate methodological support to integrate persuasion models in the design process. In contrast, there are some analytically grounded models of persuasion in the information technology domain, which can potentially be leveraged in the design process. This paper reports the development of such a methodological framework, built on existing theoretical models of persuasion. An empirical study with twenty novice designers shows discernible changes in design outcomes following the explicit implementation of the framework. Findings show that the explicit use of persuasion tools tends to shift design concepts from stand-alone solutions to more systemic solutions. A constructive shift is observed in the brainstorming process, allowing novice designers to integrate critical insights from persuasive technology tools (Captology), persuasive social psychology, and behavior change theories in their designs.
Walking through the Quadratic Assignment-Instance Space: Algorithm Performance and Landscape Measures

This paper presents an analysis of the trends and behavior of Fitness Landscape Analysis (FLA) and corresponding algorithm performance features for instances of the Quadratic Assignment Problem (QAP) and the instance space between them. Given two QAPLIB instances, a transformation generates 30 intermediary instances, i.e. problem versions for further experimentation. For each problem version, we track algorithm performance of robust tabu search (RTS) and variable neighborhood search (VNS), as well as FLA measures obtained by various types of walks. Thus, we are able to analyze how these performances and measures change during the transformation. We observe that RTS dominates VNS in earlier problem versions, while VNS outperforms RTS in later problem versions. Overall, the transformation leads to a smooth traversal of the instance space, and both algorithm performance and FLA measures correlate with problem versions.
Genetic Programming-based Evolutionary Feature Construction for Heterogeneous Ensemble Learning [Hot of the Press]

This Hof-off-the-Press paper summarizes our recently published work, "SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method," published in IEEE Transactions on Evolutionary Computation [4]. This paper presents SR-Forest, a novel genetic programming-based heterogeneous ensemble learning method, which combines the strengths of decision trees and genetic programming-based symbolic regression methods. Rather than treating genetic programming-based symbolic regression methods as competitors to random forests, we propose to enhance the performance of random forests by incorporating genetic programming as a complementary technique. We introduce a guided mutation operator, a multi-fidelity evaluation strategy, and an ensemble selection mechanism to accelerate the search process, reduce computational costs, and improve predictive performance. Experimental results on a regression benchmark with 120 datasets show that SR-Forest outperforms 25 existing symbolic regression and ensemble learning methods. Moreover, we demonstrate the effectiveness of SR-Forest on an XGBoost hyperparameter performance prediction task, which is an important application area of ensemble learning methods. Overall, SR-Forest provides a promising approach to solving regression problems and can serve as a valuable tool in real-world applications.
Comparison of MCMC Adaptation Schemes: A Preliminary Empirical Study

Adaptive Markov Chain Monte Carlo (MCMC) adapts the covariance of the proposal distribution to improve the efficiency of Metropolis Hastings (MH). Adaptive Metropolis (AM) is the prime example. Some stochastic optimisation techniques adapt the co-variance of the search distribution. Some examples are Gaussian Adaptation (GaA) and (1+1)-Covariance Matrix Adaptation Evolution Strategy (CMAES) that can be turned into MCMC samplers in a straightforward way. However, the adaptation rational used by these samplers differ. AM estimates the covariance of the target distribution based on the generated samples. GaA adapts the covariance such that the entropy of the proposal is increased/decreased when the candidate sample is accepted/rejected while adaptation in CMAES increases the likelihood of generating better search points. We compare the performance of AM, GaA and (1+1)-CMAES samplers on a test suite of target distributions to understand the effectiveness of the adaptation mechanism used.
Theory and Practice of Population Diversity in Evolutionary Computation

tutorial Free Access Share on Theory and Practice of Population Diversity in Evolutionary Computation Authors: Dirk Sudholt University of Passau, Passau, Germany University of Passau, Passau, Germany https://orcid.org/0000-0001-6020-1646Search about this author , Giovanni Squillero Politecnico di Torino, Torino, Italy Politecnico di Torino, Torino, Italy https://orcid.org/0000-0001-5784-6435Search about this author Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1361–1378https://doi.org/10.1145/3583133.3595053Published:24 July 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Limit Order Book Simulation with Generative Adversarial Networks

We propose a nonparametric method for simulating the dynamics of a limit order book using Generative Adversarial Networks (GAN) to learn the conditional distribution of the future state of the order book given its current state from time series of the limit order book. Our method yields a scenario generator for limit order books which captures a range of stylized facts and salient properties of limit order book transitions. We show that the trained generator is also able to correctly reproduce some key properties observed in empirical studies on market impact. In particular, the model exhibits a decaying marginal impact of trade size, higher impact of aggressive orders, as well as a decreasing relation between impact and order book depth.
Co-design of Physical Spaces with Neurodivergent Individuals: Application of Virtual Reality

This work makes a case for co-designing physical spaces with neurodivergent individuals through Virtual Reality (VR). The paper extends the literature by proposing the integration of VR in the design process to deliver valuable results that enable inclusive and accessible spaces with superior user experience. VR is an Industry 4.0 technology that allows us to manipulate and visualise how we experience our environment prior to building real-world infrastructure. This characteristic of VR minimises human effort and economic loss. Therefore, this work contributes to the literature by uncovering how VR could strategically aid in the co-design of built spaces with neurodivergent individuals. It addresses VR as a technology applied to prior visualisation of the built space. The main outcomes of this work are: (a) a discussion on the application of VR to create simulations compliant with the abilities of autistic individuals. (b) an understanding of potential contributions of VR in inclusive and accessible built design by leveraging the ASPECTSS™ index. (c) a research agenda for subsequent studies on integrating VR in co-designing built spaces with autistic individuals based on grounded theory.
Representations for Evolutionary Algorithms

tutorial Share on Representations for Evolutionary Algorithms Author: Franz Rothlauf Dept. Information Systems, University of Mainz, Mainz, Rheinland-Pfalz, Germany Dept. Information Systems, University of Mainz, Mainz, Rheinland-Pfalz, Germany https://orcid.org/0000-0003-3376-427XView Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1048–1068https://doi.org/10.1145/3583133.3595034Published:24 July 2023Publication History 0citation19DownloadsMetricsTotal Citations0Total Downloads19Last 12 Months19Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Gentle Introduction to Theory (for Non-Theoreticians)

tutorial Share on A Gentle Introduction to Theory (for Non-Theoreticians) Author: Benjamin Doerr Ecole Polytechnique, Palaiseau, France Ecole Polytechnique, Palaiseau, France https://orcid.org/0000-0001-5283-4208View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 946–975https://doi.org/10.1145/3583133.3595042Published:24 July 2023Publication History 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months17Last 6 weeks13 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Fitness Landscape Analysis of Genetic Programming Search Spaces with Local Optima Networks

Fitness landscape analysis (FLA) refers to a set of techniques to characterise optimisation problems. This paper presents an FLA of three types of genetic programming (GP) benchmarks: parity, symbolic regression, and artificial ant. We applied a modern graph-based FLA tool called Local Optima Networks and several classical FLA metrics (fitness distance correlation, neutrality, and ruggedness measures) to study the tree-based GP search spaces. Our analysis shows that the search spaces for all problems contain many local optima and are highly deceptive. The parity problems are highly rugged and neutral. Conversely, the problems of symbolic regression are less rugged and neutral. Finally, the artificial ant problem is highly rugged but less neutral. Our results indicate that a mutation in deep nodes makes finding the global optimum difficult.
The Crawler Strategy Based on Adaptive Immune Optimization

To solve the problem that link priority which is determined by many factors are difficult to adapt to the ever-changing web pages through fixed weights, a crawler strategy based on adaptive immune optimization is proposed. By combining the improved ant colony algorithm and adaptive immune optimization algorithm, this method achieves the comprehensive and efficient crawling of the target topic. Firstly, a retrospective ant colony algorithm which is combined by pheromone updating and positive feedback mechanism is used to crawl the web page with limited depth in a single cycle. Secondly, the factors that determine the priority of the link are abstracted as objective functions, and the adaptive immune optimization method is applied to optimize the objective functions related to the topic of the link, and the optimal solution with better convergence and distribution is obtained. Finally, the algorithm is verified by PyCharm experiment platform, and the results show that the algorithm achieves high accuracy and efficiency of crawler.
Study on a Denoising Algorithm of Improved Wavelet Threshold

When the partial discharge signal is monitored online in real-time, the signal collected by the signal receiver is frequently disturbed by the surrounding clutter. The wavelet threshold denoising technique has gained widespread adoption across various domains such as scientific research, high-end medical equipment, industrial manufacturing and other related fields with its excellent noise reduction performance since it was proposed in the last century with the advancement of industrial informatization. The threshold functions proposed earlier include hard threshold function and soft threshold function. However, as a result of the limitations inherent in the function, these two threshold functions cannot achieve the ideal goal when solving practical problems, and the reconstructed signal has a poor degree of restoration. In view of the problem that the above two threshold functions have poor noise reduction effect, a novel denoising formula based on wavelet thresholding is suggested in the present study. Matlab is used to run the simulation experiment, and several threshold algorithms are used to eliminate the noise from the partial discharge data. Two indicators are employed to assess the effectiveness of noise reduction: Signal-to-noise ratio (SNR) and mean square error (MSE). Upon comparing the experimental findings, it has been revealed that the novel threshold function surpasses the conventional approaches.
It might be virtual, but the effect is real

The application of virtual reality (VR) as a non-pharmacologic technique to reduce the need for analgesics and sedation during painful medical procedures has evolved over the past 20 years [ [1] Hitching R. Hoffman H.G. Garcia-Palacios A. et al. The emerging role of virtual reality as an adjunct to procedural sedation and anesthesia: a narrative review. J. Clin. Med. 2023; 12: 843 Crossref PubMed Scopus (8) Google Scholar ] Some studies have shown that VR as an adjunctive to analgesics reduced the pain of burn patients during wound dressings [ [2] Maani C.V. Hoffman H.G. Morrow M. et al. Virtual reality pain control during burn wound debridement of combat-related burn injuries using robot-like arm mounted VR goggles. J. Trauma. 2011; 71: S125 Crossref PubMed Scopus (132) Google Scholar ] The mechanism of action of VR is likely related to altered pain perception and its ability to distract patients from their pain-provoking reality. Functional magnetic resonance imaging (fMRI) studies showed that VR reduced pain-related brain activity, suggesting that VR modulated brain pain responses [ [3] Hoffman H.G. Richards T.L. Coda B. et al. Modulation of thermal pain-related brain activity with virtual reality: evidence from fMRI. Neuroreport. 2004; 15: 1245-1248 Crossref PubMed Scopus (243) Google Scholar ]. Virtual reality-assisted distraction during transcatheter aortic valve implantation under local anaesthesia: A randomised studyInternational Journal of CardiologyVol. 387PreviewA minimal approach, using local anaesthesia alone, has been advocated to promote faster transcatheter aortic valve replacement (TAVR) procedures in intermediate-risk patients. Pre- and periprocedural anxiety and pain remain a concern. Virtual reality (VR) is a form of non-pharmacological distraction that can potentially modulate pain and anxiety. This randomised study explored whether VR reduces pain and anxiety during TAVR without sedation and compared the effects of VR with those of standard care. Full-Text PDF
Machine Learning and Data Mining Algorithms for Geospatial Big Data

This chapter focuses on strategies to extend and adapt traditional machine learning algorithms for remote sensing and geospatial big data. Ten major strategies are discussed. They are distributed and parallel learning, data reduction and approximate computing, feature selection and feature extraction, incremental learning, deep learning, ensemble analysis, granular learning, stochastic algorithms, transfer learning, and active learning.