Reviewerly/exHarmony huggingface dataset:
Examining algorithm behavior using recurrence quantification and landscape analyses

Differences in performance between algorithms can be attributed to the interaction between their unique rule-sets and the characteristics of the instance's landscape. However, understanding this interaction can be difficult because algorithms are often composed of multiple elements, and in the worst cases are described using opaque notation and metaphors. In this paper, we introduce a methodology for the behavioral analysis of optimization algorithms, based on comparing algorithm dynamics in a given problem instance. At the methodology's core lays the hypothesis that if two algorithms, with the exact same initial conditions, have similar dynamics, then their rule-sets are also similar. An examination of Grey Wolf Optimization, shows that it exhibits bias leading to similar behavioral patterns regardless of the function.
Large scale image classification using GPU-based genetic programming

Genetic programming (GP) has been applied to image classification and achieved promising results. However, most GP-based image classification methods are only applied to small-scale image datasets because of the limits of high computation cost. Efficient acceleration technology is needed when extending GP-based image classification methods to large-scale datasets. Considering that fitness evaluation is the most time-consuming phase of the GP evolution process and is a highly parallelized process, this paper proposes a CPU multiprocessing and GPU parallel approach to perform the process, and thus effectively accelerate GP for image classification. Through various experiments, the results show that the highly parallelized approach can significantly accelerate GP-based image classification without performance degradation. The training time of GP-based image classification method is reduced from several weeks to tens of hours, enabling it to be run on large-scale image datasets.
Zeroth-order covariance matrix adaptation evolution strategy for single objective bound constrained numerical optimization competition

The paper represents a competition entry for the competition on bound constrained single objective numerical optimization at The Genetic and Evolutionary Computation Conference (GECCO) 2022[2] by a hybrid algorithm named Zeroth-Order Covariance Matrix Adaptation Evolution Strategy (ZO-CMAES).
Quantum neuron selection

Gradient descent methods have long been the de facto standard for training deep neural networks. Millions of training samples are fed into models with billions of parameters, which are slowly updated over hundreds of epochs. Recently, it's been shown that large, randomly initialized neural networks contain subnetworks that perform as well as fully trained models. This insight offers a promising avenue for training future neural networks by simply pruning weights from large, random models. However, this problem is combinatorically hard and classical algorithms are not efficient at finding the best subnetwork. In this paper, we explore how quantum algorithms could be formulated and applied to this neuron selection problem. We introduce several methods for local quantum neuron selection that reduce the entanglement complexity that large scale neuron selection would require, making this problem more tractable for current quantum hardware.
Graph-based genetic programming

tutorial Open Access Share on Graph-based genetic programming Authors: Roman Kalkreuth TU Dortmund University, Dortmund, Germany and Brno University of Technology, Brno, Czech Republic TU Dortmund University, Dortmund, Germany and Brno University of Technology, Brno, Czech RepublicView Profile , Léo Françoso Dal Piccol Sotto TU Dortmund University, Dortmund, Germany and Brno University of Technology, Brno, Czech Republic TU Dortmund University, Dortmund, Germany and Brno University of Technology, Brno, Czech RepublicView Profile , Zdeněk Vašíček TU Dortmund University, Dortmund, Germany and Brno University of Technology, Brno, Czech Republic TU Dortmund University, Dortmund, Germany and Brno University of Technology, Brno, Czech RepublicView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 958–982https://doi.org/10.1145/3520304.3533657Published:19 July 2022Publication History 0citation46DownloadsMetricsTotal Citations0Total Downloads46Last 12 Months46Last 6 weeks7 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Evolutionary operation setting for outcome accumulation type evolutionary rule discovery method

Association rule analysis has been widely employed as a basic technique for data mining. Extensive research has also been conducted to apply evolutionary computing techniques to the field of data mining. This study presents a method to evaluate the settings of evolutionary operations in evolutionary rule discovery method, which is characterized by the execution of overall problem solving through the acquisition and accumulation of small results. Since the purpose of population evolution is different from that of general evolutionary computation methods that aim at discovering elite individuals, we examined the difference in the concept of settings during evolution and the evaluation of evolutionary computation by visualizing the progress and efficiency of problem solving. The rule discovery method (GNMiner) is characterized by reflecting acquired information in evolutionary operations; this study determines the relationship between the settings of evolutionary operations and the progress of each task execution stage and the achievement of the final result. This study obtains knowledge on the means of setting up evolutionary operations for efficient rule-set discovery by introducing an index to visualize the efficiency of outcome accumulation. This implies the possibility of setting up dynamic evolutionary operations in the outcome accumulation-type evolutionary computation in future studies.
Evolution of neural networks

tutorial Share on Evolution of neural networks Author: Risto Miikkulainen The University of Texas at Austin and Cognizant Al Labs The University of Texas at Austin and Cognizant Al LabsView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1020–1037https://doi.org/10.1145/3520304.3533656Published:19 July 2022Publication History 0citation50DownloadsMetricsTotal Citations0Total Downloads50Last 12 Months50Last 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
On stochastic evolving algorithms

This pioneering study tries to break the wall of the question, how to develop algorithms capable of self-improving. The foundation of this work is the extended model of the human mind, while the information flow between components is inspired by molecular genetics. As a result, the proposed evolving algorithms consist of complex rules and stochastic processing, while the preliminary results also revealed enormous potential for the future.
Automatically exploring computer system design spaces

While much research has focused on using search to optimize software, it is possible to use these same approaches to optimize other parts of the computer system. With decreased costs in silicon customization, and the return of centralized systems carrying out specialized tasks, the time is right to begin thinking about tools to optimize systems for the needs of specific software targets. In the approach outlined, the standard Genetic Improvement process is flipped with source code considered static and the remainder of the computer system altered to the needs of the software. The project proposed is preliminary research into incorporating grammar-based GP with an advanced computer architecture simulator to automatically design computer systems. I argue this approach has the potential to significantly improve the design of computer systems while reducing manual effort.
Trimming, ordering, and similarity check for DSMGA-II

The dependency structure matrix genetic algorithm II (DSMGA-II) is one of the state-of-the-art model-building genetic algorithms capable of solving combinatorial optimization problems by exploiting the underlying structures of the problems. The linkage model generates a series of masks for trial to recombine genes among chromosomes via optimal mixing. This paper proposes three improvements that adaptively adjust the scope, the order, and the receivers of trials. Specifically, the mean of the mask sizes from previous successful recombinations is used to limit the maximum sizes of later trials. Also, successful recombinations prioritize the corresponding mask sizes of trials. Finally, recombinations are confined between chromosomes that pass the proposed similarity check. The ablation study indicates that each proposed technique is indispensable. Combined with these three improvements, DSMGA-II-2E empirically consumes fewer function evaluations on most of the test problems.
Runtime analysis of population-based evolutionary algorithms

tutorial Share on Runtime analysis of population-based evolutionary algorithms Authors: Per Kristian Lehre University of Birmingham, Birmingham, UK University of Birmingham, Birmingham, UKView Profile , Pietro S. Oliveto University of Sheffield, Sheffield, UK University of Sheffield, Sheffield, UKView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1398–1426https://doi.org/10.1145/3520304.3533658Published:19 July 2022Publication History 0citation27DownloadsMetricsTotal Citations0Total Downloads27Last 12 Months27Last 6 weeks3 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Chaos engineering

We present our Chaos Engineering framework that accelerates the emergence of resilient strategic plans to maintain high levels of performance given the turbulent and unpredictable conditions in real-world military deployments. Our proposed Chaos Engine will stress agents by intelligently searching over scenario chaos factors reflecting real-world events such as weather changes, loss of communication, basing and access, platform failures, and enemy force composition and mission plans. Our work is split into four technical phases: 1) Evaluate: model and characterize agent performance in adaptively generated simulation environments; 2) Challenge: systematically find chaos factors that expose failures; 3) Fitness: balance challenge and feasibility; and 4) Diversity: maintain a population of scenarios that expose the agent to a range of challenges. The work presented here focuses on the Challenge phase and provides preliminary results for our adaptive domain generation paradigm, known as the adversarial architect. The adversarial architect will use an evolutionary-based optimizer to generate diverse scenarios that start simple and become more challenging as agents learn. We demonstrate the efficacy of our approach in the Acrobot domain, where we show the adversarial architect generating an Auto Curricula of inertial parameters that effectively guides the learning agent to resilient and effective policies.
Generative hyper-heuristics

tutorial Share on Generative hyper-heuristics Authors: Daniel R. Tauritz Auburn University Auburn UniversityView Profile , John Woodward Queen Mary University of London Queen Mary University of LondonView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1111–1140https://doi.org/10.1145/3520304.3533646Published:19 July 2022Publication History 0citation29DownloadsMetricsTotal Citations0Total Downloads29Last 12 Months29Last 6 weeks3 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
AbstractSwarm multi-agent logistics competition entry

The AbstractSwarm Framework [1] was developed to study multi-agent simulation for optimizing logistics scenarios, with a special focus on (but not restricted to) hospital logistics. The basics of a solution to the accompanying competition [2] and the included benchmark problems is to be presented in this paper. To achieve this, Q-Learning [5, 7, 8] is used to determine scores for possible actions an agent can take depending on the state of the AbstractSwarm Graph representing the environment. Every action receives multiple such scores which are in turn aggregated, hence the name "QPlus".
Coevolutionary computation for adversarial deep learning

tutorial Open Access Share on Coevolutionary computation for adversarial deep learning Authors: Jamal Toutouh University of Málaga, Málaga, Spain University of Málaga, Málaga, SpainView Profile , Una-May O'Reilly MIT CSAIL MIT CSAILView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1487–1505https://doi.org/10.1145/3520304.3533651Published:19 July 2022Publication History 0citation42DownloadsMetricsTotal Citations0Total Downloads42Last 12 Months42Last 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
A comparative study of GP-based and state-of-the-art classifiers on a synthetic machine learning benchmark

In this paper we compare performance of genetic programming-based symbolic classifiers on a novel synthetic machine learning benchmark called DIGEN. This framework and collection of 40 different classification problems was designed specifically to differentiate performance of leading machine learning methods.
The diversity-accuracy duality in ensembles of classifiers

A typical approach to defining a classifier ensemble requires two phases: creating a large set of potential classifiers, then selecting an appropriate subset to form an ensemble. It is generally accepted that the ensemble should be behaviourally diverse in order to deliver maximum accuracy, hence evolutionary search methods have been used to explicitly create diversity. Here we investigate a range of search methods that span the full spectrum of favouring accuracy, diversity, or different combinations of both, across both phases defined above. Surprisingly, we show that multiple unique combinations between a diversity metric and accuracy give rise to similar results, including cases that assign the most importance to accuracy. This equivalence between utilising diversity or accuracy objectives enables us to posit the existence of a diversity-accuracy duality in ensembles of classifiers, which challenges notions about the need to find a trade-off between the two.
Cost-sensitive classification tree induction as a bi-level optimization problem

Data imbalance is still so far a challenging issue in data classification. In literature, cost-sensitive approach has been used to deal with such a challenge. Despite its interesting results, the manual design of cost matrices is still the main shortcoming of this approach. The data engineer is still facing a great difficulty in defining the misclassification costs, especially with the absence of domain specific knowledge. Recent works suggest the use of genetic programming as an effective tool to design classification trees with automatically learned costs. Although promising results were obtained, evaluating a classification tree with a single cost matrix is not a wise choice. Indeed, the tree quality evaluation requires trying several misclassification cost matrices to be more precise and fair. Motivated by this observation, we propose in this paper a bi-level modeling of the cost-sensitive classification tree induction problem where the upper level evolves the classification trees, while the cost matrix of each tree is optimized at the lower level. Our bi-level modeling is solved using an existing co-evolutionary algorithm, and the resulting method is named Bi-COS. The obtained comparative experimental results on several imbalanced benchmark datasets show the merits of Bi-COS with respect to the state-of-the art.
Genetic programming convergence

We study both genotypic and phenotypic convergence in GP floating point continuous domain symbolic regression over thousands of generations. Subtree fitness variation across the population is measured and shown in many cases to fall. In an expanding region about the root node, both genetic opcodes and function evaluation values are identical or nearly identical. Bottom up (leaf to root) analysis shows both syntactic and semantic (including entropy) similarity expand from the outermost node. Despite large regions of zero variation, fitness continues to evolve and near zero crossover disruption suggests improved GP systems. W. B. Langdon. 2022. Genetic Programming Convergence. GP & EM 23, 1, 71--104.
On generalizing the power function exponent constructions with genetic programming

Many works are investigating Almost Perfect Nonlinear (APN) functions and, in particular, APN power functions. Such functions are of the form F(x) = xd, and they have practical relevance as they reach in characteristic 2 the best possible differential uniformity. This work investigates whether genetic programming (GP) can "reinvent" the known expressions used to obtain exponent values d resulting in APN functions. The ultimate goal is to find classes of exponents that would be "transversal" to the known infinite classes of APN exponents, and would contain new APN exponents (for values of n necessarily larger than those for which an exhaustive search could be made so far). This would be already a breakthrough, and our hope is to find this way new infinite classes of APN exponents. Our results show this is possible but difficult, and a careful trade-off between finding new values and skipping known values is required.
Neighbours similar fitness and the effectiveness of restarting local search

Premature convergence can be detrimental to the performance of search methods, which is why many search algorithms include restart strategies to deal with it. While it is common to perturb the incumbent solution with diversification steps of various sizes with the hope that the search method will find a new basin of attraction leading to a better local optimum, it is usually not clear whether this strategy is effective. To establish a connection between restart effectiveness and properties of a problem, we introduce a new property of fitness landscapes termed Neighbours with Similar Fitness. We conjecture that this property is true for many PLS (polynomial-time local search)-complete problems, and we demonstrate that the effectiveness of a restart strategy depends on this property.
A preliminary study of prediction interval methods with genetic programming

This article presents an exploratory study on modelling Prediction Intervals (PI) with two Genetic Programming (GP) methods. A PI is the range of values in which the real target value is expected to fall into. It should combine two contrasting properties: to be as narrow as possible and to include as many data observations as possible. One proposed GP method, called CWC-GP, evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure that combines the width and the probability coverage of the PI. The other proposed GP method, called LUBE-GP, evolves independently the boundaries of the PI with a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied with Direct and Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the presented preliminary results pave the way to further investigations. The most promising results were observed with the Sequential CWC-GP.
Improving the differential evolution strategy by coupling it with CMA-ES

Differential Evolution Strategy (DES) is a method that combines the differential mutation with the search direction adaptation mechanisms used by the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Although earlier research on that algorithm proved its good efficiency, it was still outperformed by the combined and hybrid methods which have been the winners of single objective bound constrained numerical optimization competitions. This paper reports on research that was aimed at improving the efficiency of DES in such a way that the optimization process is initially performed by DES, and after it terminates, the result is finely tuned by CMA-ES, whose expectation vector and the covariance matrix are initialized with statistics of points generated by DES. The hybrid method is evaluated according to the problem definitions and evaluation criteria for the 2022 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization. According to the numerical results, the proposed hybrid method outperforms the standard versions of both DES and CMA-ES. Moreover, the comparison of results on the CEC'2017 benchmark suite evidences that the presented method would be superior or comparable to other methods whose results for CEC'2017 have been reported by the competing teams.
Automated configuration of genetic algorithms by tuning for anytime performance

This paper summarizes our work "Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance", to appear in IEEE Transactions on Evolutionary Computation.