Reviewerly/exHarmony huggingface dataset:
Introduction to Quantum Optimization

tutorial Open Access Share on Introduction to Quantum Optimization Authors: Alberto Moraglio University of Exeter, Exeter, UK University of Exeter, Exeter, UK https://orcid.org/0000-0003-4782-6590View Profile , Francisco Chicano University of Málaga, Málaga, Spain University of Málaga, Málaga, Spain https://orcid.org/0000-0003-1259-2990View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1433–1462https://doi.org/10.1145/3583133.3595040Published:24 July 2023Publication History 0citation62DownloadsMetricsTotal Citations0Total Downloads62Last 12 Months62Last 6 weeks53 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
Lexicase Selection

tutorial Share on Lexicase Selection Authors: William George La Cava Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA https://orcid.org/0000-0002-1332-2960View Profile , Thomas Helmuth Hamilton College, Clinton, NY, USA Hamilton College, Clinton, NY, USA https://orcid.org/0000-0002-2330-6809View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 976–989https://doi.org/10.1145/3583133.3595035Published:24 July 2023Publication History 0citation29DownloadsMetricsTotal Citations0Total Downloads29Last 12 Months29Last 6 weeks9 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
Decision Trees+Evolutionary Algorithm for Predict then Optimize

We present a collaboration between decision trees and an evolutionary algorithm inspired by Smart "Predict then Optimize" framework. In this work, we analyze the requirements for designing an evolutionary algorithm to solve complex combinatorial problem which can change over the time. We study the effect of changing the parameters of the objective function and how we can adapt the evolutionary algorithm to efficiently tackle new conditions of the problem. To predict the parameters of the objective function we use a decision tree. We evaluate our collaborative schema using instances for the shortest path problem and compare it with recently published work that uses complete techniques, obtaining encouraging results.
ATLAS - A Co-evolutionary Framework for Automatic Tuning of Adversarial Neural Networks

Generative Adversarial Networks (GANs) have gained popularity due to their ability to produce realistic examples from existing data without any supervision. However, they are dependent on their hyperparameters, the tuning of which is usually a manual task. Additionally, the computing resources required for such training are also extremely high. In this paper, ATLAS - a Cloud-based Co-evolutionary Framework for training such adversarial networks using Evolutionary Algorithms is proposed. ATLAS views the GAN components (generator and discriminator) as in a predator-prey relationship and involves co-evolution as a method to address the challenges of overfitting, exploding/vanishing gradients and tunes the hyperparameters of both the components of the GAN. The ATLAS framework is designed to be customizable, and resource flexible to allow for set-up and easy usage for training complex adversarial networks in both distributed and cloud environments. Experiments testing ATLAS capability for anomaly detection were performed and the results show that ATLAS can consistently evolve and produce high-performance GAN models.
Designing Blended Environments: Cultural Heritage as a Tool to Create Humane Virtual Spaces and Tech-inspired Seamlessness in Tangible Products

The omnipresence of technology has resulted in the development of the “phygital” realm—an ecosystem of connected physical places and digital spaces, creating opportunities to design efficient and emotionally charged experiences. Using the accelerating digitalism, artists and designers have been exploring the correlation between one’s identity and the digital realm, and how it will evolve in the future. The study in this paper examines how the visual language of both tangible and virtual spaces impacts our perception of space and overall experience. While physical products and spaces continue to incorporate technology to build an omnichannel presence, the digital world is perceived as a rather machine-led virtual environment lacking human presence and quality. This paper proposes the idea of a symbiotic exchange between the visual language of the physical and the digital to create fluid hybrid environments—modifying the aesthetic of tangible products and spaces to create a sense of tech-inspired seamlessness, while virtual environments can draw from the time-honored cultural heritage, to incorporate nature and humanity in the cyberspace. Cultural heritage can be used to exploit the typical advantages of both virtual and physical reality, ensuring a smooth transition for the users into blended environments in the future.
Hot off the Press: From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds

Due to the more complicated population dynamics of the NSGA-II, none of the existing runtime guarantees for this algorithm is accompanied by a non-trivial lower bound. Via a first mathematical understanding of the population dynamics of the NSGA-II, that is, by estimating the expected number of individuals having a certain objective value, we prove that the NSGA-II with suitable population size needs Ω (Nn log n) function evaluations to find the Pareto front of the OneMinMax problem and Ω(Nnk) evaluations on the OneJumpZeroJump problem with jump size k. These bounds are asymptotically tight (that is, they match previously shown upper bounds) and show that the NSGA-II here does not even in terms of the parallel runtime (number of iterations) profit from larger population sizes. For the OneJumpZeroJump problem and when the same sorting is used for the computation of the crowding distance contributions of the two objectives, we even obtain a runtime estimate that is tight including the leading constant.
Research and Application of Rule Engine Technology and Data Distribution Method of Power Internet of Things Based on Flink

In the construction of the power Internet of Things, it is necessary to face the unified access of massive IoT terminals in power generation, power transmission and transformation, power distribution and other disciplines. The terminals have many types, wide distribution, large quantity, and heterogeneous data. The IoT management platform faces huge pressure in terminal management and data processing. Due to the large number of power grid equipment, terabytes of data are generated every day. How to timely and accurately distribute these data to the upper business system is the key problem that the IOT management platform must solve. In view of the above problems and requirements, this paper proposes a rule engine technology and data distribution method based on Flink, which uses Flink’s stream processing technology and SQL semantics to create data distribution rules to distribute different business data to different application systems. The application results show that the scheme is flexible in configuration and can meet the requirements of different business scenarios. At the same time, Flink’s exact once semantics ensure safe and efficient data transmission. By setting reasonable parallel operators, it can effectively improve data forwarding performance and ensure that data is not lost or overstocked during transmission.
Research Progress of Semantic Image Synthesis

Semantic image synthesis is a cutting-edge technique that employs semantic labels to generate realistic images. This technique has gained widespread popularity in the field of image synthesis, and has become a powerful tool for producing diverse and creative images. With the continued advancement of deep generation models, more sophisticated techniques are being developed for semantic image synthesis. In this paper, we introduce the fundamental concepts underlying diffusion-based and adversarial generative networks-based semantic image synthesis techniques. Additionally, we analyze the distinctive features of various models, propose two visualization schemes for generative models, and address the issue of pattern-running. Finally, we provide a summary and future outlook on the challenges and development trends of deep generative models, offering valuable insights for both researchers and practitioners.
An Efficient Specific Pedestrian Tracking Fusion Algorithm

This paper proposes an effective pedestrian tracking algorithm based on multi-algorithm fusion to address potential issues in the task of tracking a specific pedestrian in complex backgrounds, particularly when there are multi-scale targets, deformed targets, occluded targets, and long-time tracking targets. The algorithm uses MobileNet to lightweight the YOLOv3 detection network, and integrates the detection information acquired at regular intervals with the KCF tracking information, so as to quickly update and fix the target pedestrian position. Additionally, the algorithm proposes a target selection strategy to determine the precise location of the occluded pedestrian, and establishes an occlusion update strategy to address any possible continuous occlusion. The contrast tracking experiments in multiple scenes show that the proposed tracking fusion algorithm has good real-time and robustness.
Exploratory Landscape Analysis Based Parameter Control

Parameter tuning in evolutionary algorithms is a very important topic, as the correct choice of parameters greatly affects their performance. Fitness landscape analysis can help identify similar problems and allow for gathering problem structure insights for fitness-aware optimization algorithm parameter choice.
Analysis of Path Optimization Problem Based on Reinforcement Learning

Path planning is to enable the target object to find a safe collision-free path from the beginning to the end within the specified area at the minimum time or distance cost. This paper elaborates the path planning technology based on conventional methods and reinforcement learning methods, combines the game of crossing the desert, divides the reinforcement learning methods into two categories: value-based and strategy-based, adopts Q-Learning value-based representative methods and strategy-based representative methods such as strategy gradient and imitation learning, and analyzes the development status of fusion strategies and deep reinforcement learning methods. Based on the Q-learning reinforcement learning method, this paper studies the problem of path optimization through the desert, and establishes a mathematical model to propose different path strategies, on the basis of satisfying the various constraints in the problem, the Q-learning reinforcement learning algorithm is used to train 500 times, the best path strategy is given, and finally the path optimization problem is simulated and evaluated, indicating that reinforcement learning can be successfully applied to the path optimization problem.
A New Metaheuristic-Algorithm Similarity Measure Using Signal Flow Diagrams

The component-based view for metaheuristic research promotes the identification of structural components of metaheuristics and metaheuristic-algorithms for analysis. In this study, we propose a method for measuring similarity between metaheuristic-algorithms. The method is based on a modified version of a signal flow representation of metaheuristic-algorithms that is aligned with the component-based view. The method takes any two metaheuristic-algorithms and decomposes them into their heuristic components whilst taking note of the order of execution of the heuristics. Features of the heuristics are then extracted, and finally a feature-based similarity calculation, that also considers the position of the heuristics, is performed to obtain an overall similarity score between the two metaheuristic-algorithms. The method incorporates more structural information in the similarity calculation than previous component-wise similarity measures and can be extended to cover a comprehensive set of metaheuristic components.
Assessing the Generalizability of a Performance Predictive Model

A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model. The predictive model uses the feature representation of a set of problem instances as input data and predicts the algorithm performance achieved on them. Common machine learning models struggle to make predictions for instances with feature representations not covered by the training data, resulting in poor generalization to unseen problems. In this study, we propose a workflow to estimate the generalizability of a predictive model for algorithm performance, trained on one benchmark suite to another. The workflow has been tested by training predictive models across benchmark suites and the results show that generalizability patterns in the landscape feature space are reflected in the performance space.
Towards optimisers that `Keep Learning'

We consider optimisation in the context of the need to apply an optimiser to a continual stream of instances from one or more domains, and consider how such a system might 'keep learning': by drawing on past experience to improve performance and learning how to both predict and react to instance and/or domain drift.
Multi-Step-Ahead Web Service QoS Time Series Forecasting: A Multi-Predictor-Based Genetic Programming Approach

Previously, in a GECCO 2022 Hot-off-the-Press paper [1], we presented a comprehensive survey of the modeling and prediction of Web service (WS) quality of service (QoS) time series [2]. Based on the exhaustive investigation in [2], this research subject has already been deeply and widely studied for over a decade; for the one-step-ahead version of this problem, which can be considered its most primitive problem form, overall, our proposed and developed genetic programming (GP)-based solution outperforms competitors in terms of both modeling and forecasting accuracy, according to our ongoing study, which has been reported in [3] [4] [5]. Nevertheless, as argued in [6], for the long-term use and rental of cloud-based WSs, multi-step-ahead QoS time series prediction of these services is needed. Thus, the authors employed and revised the two most widely used single-predictor-based time series methods, namely, autoregressive integrated moving average (ARIMA) models and exponential smoothing (ES), to address this latest version of the problem.
Extending Tree-Based Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors

Automated machine learning (AutoML) has allowed for many innovations in biomedical data science; however, most AutoML approaches do not support image or text data. To rectify this, we implemented four feature extractors in the Tree-based Pipeline Optimization Tool (TPOT) to make TPOT with Feature Extraction (TPOT-FE), an automated machine learning system that uses genetic programming (GP) to create ideal pipelines for a classification or regression task. These feature extractors enable TPOT-FE to build pipelines that can analyze non-tabular data, including text and images, which are increasingly common biomedical big data modalities that can contain rich quantities of information. We evaluate this approach on six image datasets and four text datasets, including three biomedical datasets, and show that TPOT-FE is able to consistently construct and optimize classification pipelines on all of the datasets.
On the Trade-Off between Population Size and Number of Generations in GP for Program Synthesis

When using genetic programming for program synthesis, we are usually constrained by a computational budget measured in program executions during evolution. The computational budget is influenced by the choice of population size and number of generations per run leading to a trade-off between both possibilities. To better understand this trade-off, we analyze the effects of different combinations of population sizes and number of generations on performance. Further, we analyze how the use of different variation operators affects this trade-off. We conduct experiments on a range of common program synthesis benchmarks and find that using larger population sizes lead to a better search performance. Additionally, we find that using high probabilities for crossover and mutation lead to higher success rates. Focusing on only crossover or using only mutation usually leads to lower search performance. In summary, we find that large populations combined with high mutation and crossover rates yield highest GP performance for program synthesis approaches.
Discovering Evolution Strategies via Meta-Black-Box Optimization

Optimizing functions without access to gradients is the remit of black-box methods such as evolution strategies. While highly general, their learning dynamics are often times heuristic and inflexible --- exactly the limitations that meta-learning can address. Hence, we propose to discover effective update rules for evolution strategies via meta-learning. Concretely, our approach employs a search strategy parametrized by a self-attention-based architecture, which guarantees the update rule is invariant to the ordering of the candidate solutions. We show that meta-evolving this system on a small set of representative low-dimensional analytic optimization problems is sufficient to discover new evolution strategies capable of generalizing to unseen optimization problems, population sizes and optimization horizons. Furthermore, the same learned evolution strategy can outperform established neuroevolution baselines on supervised and continuous control tasks. As additional contributions, we ablate the individual neural network components of our method; reverse engineer the learned strategy into an explicit heuristic form, which remains highly competitive; and show that it is possible to self-referentially train an evolution strategy from scratch, with the learned update rule used to drive the outer meta-learning loop.
Neuroevolution for Autonomous Cyber Defense

This work presents the preliminary results of and discusses current challenges in ongoing research of neuroevolution for the task of evolving agents for autonomous cyber operations (ACO). The application of reinforcement learning to the cyber domain is especially challenging due to the extremely limited observability of the environment over extended time frames where an adversary can potentially take many actions without being detected. To promote research within this space The Technical Cooperation Program (TTCP), which is an international collaboration organization between the US, UK, Canada, Australia, and New Zealand, released the Cyber Operations Research Gym (CybORG) to enable experimentation with RL algorithms in both simulated and emulated environments. Using competition to spur investigation and innovation, TTCP has released the CAGE Challenges which for evaluating RL in network defense.[1] This work evolves agents for ACO using the python-based neuroevolution library Evosax[2] which supports high performance, GPU accelerated evolutionary algorithms for the purpose of optimizing artificial neural network parameters. The use of neuroevolution in this paper is a first for the ACO task and benchmarks two popular algorithms to identify factors which impact their effectiveness.
Automated algorithm composition of unsupervised image clustering algorithms

Unsupervised learning algorithms are popular as they do not require annotated data. However as per the no-free lunch theorem, the best algorithm to use is not the same for all datasets. This study is the first to automate the composition of an unsupervised image clustering algorithm. This work uses two different techniques to perform automated algorithm composition. The first technique is a genetic algorithm (GA) and the second is a genetic algorithm hyperheuristic (GAHH). A comparison of the two techniques shows that the GA outperforms the GAHH. The GA designs unsupervised clustering algorithms that result in state of the art performance for the Oral lesion, Celebrity faces and COVID-19 datasets.
Transforming Industrial Operations with Blockchain: The Healthcare Application Health-Chain

Globally, effective application of cloud technology, mobile, social media, analytics, and IoT (Internet of Things), has resulted in new creative products, tight consumer interactions, and enhanced corporate operations. Block-chain technology is reshaping the digital landscape by offering a fresh perspective on efficiency, system security and resilience. Despite its initial popularity as a foundation for digital money, Blockchain is much more than that. It offers a safe way to conduct any form of product or service trade. Growing laws, cybercrime, and fraud are impeding industrial progress even while trusted connections are becoming more and more crucial for growth. To overcome these obstacles, Block-chain will enable more flexible better customer interactions, cloud technology, value chains, quicker product developments, and quicker IoT and integration. Further Block-chain reduces the charge of commerce by providing a reliable bond that is handled devoid of outside interference from parties that might not directly provide value. It is now feasible to create engagements, smart contracts, and agreements with solid cyber-security components built in. In order to show and illustrate the usage of Block-chain technology across numerous industrial applications, this research attempts to build the framework for that. The IBM Block-chain project is used to define and develop the healthcare application Health-chain. The ideas may be used to a variety of trades, including as manufacturing, banking, and government, where scalability, safety, and efficiency are needed.
Evolving Nonlinear Multigrid Methods With Grammar-Guided Genetic Programming

We formulate a formal grammar to generate Full Approximation Scheme multigrid solvers. Then, using Grammar-Guided Genetic Programming we perform a multiobjective optimization to find optimal instances of such solvers for a given nonlinear system of equations. This approach is evaluated for a two-dimensional Poisson problem with added nonlinearities. We observe that the evolved solvers outperform the baseline methods by having a faster runtime and a higher convergence rate.