Reviewerly/exHarmony huggingface dataset:
Accelerating Gene-pool Optimal Mixing Evolutionary Algorithm for Neural Architecture Search with Synaptic Flow

This study experiments the integration of the zero-cost proxy metric Synaptic Flow with the Gene-pool Optimal Mixing (GOM) crossover to efficiently generate new candidates during an evolutionary neural architecture search (ENAS). Our experiments demonstrate that the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) with Synaptic Flow can obtain top-performing architectures with a small additional overhead compared to a classic Genetic Algorithm. Code is available at: https://github.com/ELO-Lab/SF-GOMENAS.
Nearest better network for visualization of the fitness landscape

This paper proposes a general method for the visualization fitness landscapes. The method transforms the original fitness landscape to a 3D visualized network from the perspective of the optimization algorithms. This method can visualize the fitness landscapes of continuous problems of different dimensions, combinatorial problems and even the mixed integer problems. Experiments show that the proposed method can maintain most of the critical properties of problems, such as modality, ruggedness and neutrality.
Analyzing the interplay between transferable GANs and gradient optimizers

Recently, different neuroevolutionary approaches have been proposed to enhance the architecture and hyperparameters of generative neural models. Many of these approaches have to train the candidate architectures before their evaluation. The gradient descent algorithm used to do so is often overlooked in the evolutionary procedure, despite being a critical aspect of the training. In this paper, we investigate the role played by the gradient-based optimizer chosen to train a generative adversarial network when its architecture has been obtained using neuroevolution. We focus on 2D Gaussian mixture approximation problem and evaluate the effect of a set of representative gradient-based techniques on the quality of the performance of the GANs. Our results show that the particular choice of the gradient optimizer can be as relevant as the appropriate selection of the architecture.
Experimental Analyses of Crossover and Diversity on Jump

While it is mathematically proven that the (μ + 1) GA optimizes Jumpk efficiently for low crossover probabilities, theory research still struggles with the analysis of crossover-based optimization for high crossover probabilities on this key test function. Research in this area has improved our understanding of crossover in general, in particular regarding the emergence of diversity, the crucial ingredient for successful optimization with genetic algorithms.
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite

Box-constraints limit the domain of decision variables and are common in real-world optimization problems, for example, due to physical, natural or spatial limitations. Consequently, solutions violating a box-constraint may not be evaluable. This assumption is often ignored in the literature, e.g., existing benchmark suites, such as COCO/BBOB, allow the optimizer to evaluate infeasible solutions. This paper presents an initial study on the strict-box-constrained benchmarking suite (SBOX-COST), which is a variant of the well-known BBOB benchmark suite that enforces box-constraints by returning an invalid evaluation value for infeasible solutions. Specifically, we want to understand the performance difference between BBOB and SBOX-COST as a function of two initialization methods and six constraint-handling strategies all tested with modular CMA-ES. We find that, contrary to what may be expected, handling box-constraints by saturation is not always better than not handling them at all. However, across all BBOB functions, saturation is better than not handling, and the difference increases with the number of dimensions. Strictly enforcing box-constraints also has a clear negative effect on the performance of classical CMA-ES (with uniform random initialization and no constraint handling), especially as problem dimensionality increases.
Do Random and Chaotic Sequences Really Cause Different PSO Performance?

Our topic is performance differences between using random and chaos for particle swarm optimization (PSO). We take random sequences with different probability distributions and compare them to chaotic sequences with different but also with same density functions. This enables us to differentiate between differences in the origin of the sequences (random number generator or chaotic nonlinear system) and statistical differences expressed by the underlying distributions. Our findings (obtained by evaluating the PSO performance for various benchmark problems using statistical hypothesis testing) cast considerable doubt on previous results which compared random to chaos and suggested that the choice leads to intrinsic differences in performance.
Knowledge Shaped Behavior Generation for Online Adaptation

Many real world robotics problems, where the mission needs or environmental conditions change suddenly, require robots to identify deficiencies and adapt their behaviors in order to satisfy the new task requirements. Quality Diversity (QD) methods can generate diverse sets of behaviors that augment a robot's initial skills; however, they require many training runs and result in hundreds or thousands of candidate behaviors the robot would need to try. We introduce Knowledge Shaped MAP-Elites, which exploits the knowledge past policies no longer work to shape a small number of niches in the behavior space; this generates new behaviors without any direct information of the target task. Knowledge Shaped MAP-Elites reliably finds behaviors that work on an unknown target task in an order of less magnitude computation compared to state-of-the-art QD approaches.
Cost-Aware Active Learning for Feasible Region Identification

Design space exploration for engineering design involves identifying feasible designs that satisfy design specifications, often represented by feasibility constraints. To determine whether a design is feasible, an expensive simulation is required. Therefore, it is crucial to find and model the feasible region with as few simulations as possible.
Revealing the Inner Dynamics of Evolutionary Algorithms with Convection Selection

Evolutionary algorithms are stochastic algorithms so they tend to find different solutions when run repeatedly. However, it is not just the solutions that vary - the very dynamics of the search that led to finding these solutions are likely to differ as well. It is especially in the algorithms with complex population structures - such as convection selection where a population is divided into subpopulations according to fitness values - where an opportunity for highly diverse dynamics arises. This work investigates the way evolutionary dynamics of subpopulations influence the performance of evolutionary algorithms with convection selection. We employ a demanding task of evolutionary design of 3D structures to analyze the relation between the properties of the optimization task and the features of the evolutionary process. Based on this analysis, we identify the mechanisms that influence the performance of convection selection, and suggest ways to improve this selection scheme.
Evolution of an Internal Reward Function for Reinforcement Learning

Artificial neural networks (ANNs) can be trained with reinforcement learning (RL) in simulation to control robots. However, changes in the environment resulting in out-of-distribution situations put learned policies at risk of failure. Since the world can change in unpredictable ways, it might be desirable to be able to continue to update the parameters of the ANNs even after deployment, to prevent failures stemming from a distributional shift. However, in order to optimize with RL, a reward signal is needed. This is usually provided in the simulated environment, but might not necessarily always be available after training. We propose a solution to this problem that involves evolving a function that provides a reward signal to an RL algorithm based only on the inputs and outputs of the policy. We call this approach Evolved Internal Reward Reinforcement Learning (EIR-RL) and test it on various control tasks that have different reward structures and difficulty levels. Our method shows improved training stability and speed of the RL agent under standard circumstances, as well as the ability to train the RL agent under circumstances unseen during the initial optimization phase. We discuss how our results could inform future studies on autonomous, adapting agents.
Tree-Based Grammatical Evolution with Non-Encoding Nodes

Grammar-guided genetic programming is a type of genetic programming that uses a grammar to restrict the solutions in the exploration of the search space. Different representations of grammar-guided genetic programming exist, each with specific properties that affect how the evolutionary process is developed. We propose a new representation that uses a tree structure with non-encoding nodes for the individuals in the population, a.k.a. Tree-Based Grammatical Evolution with Non-Encoding Nodes. Each tree's node has a set of children nodes and an associated number that determines which are used in decoding the solution and which are non-encoding nodes. This representation increases the size and complexity of the individuals while performing a more exhaustive exploration of the solution space. We compare the performance of our proposal with state-of-the-art genetic programming algorithms for the 11-multiplexer benchmark, showing encouraging results.
Landscape Analysis of Optimization Problems and Algorithms

tutorial Open Access Share on Landscape Analysis of Optimization Problems and Algorithms Authors: Katherine Malan University of South Africa, Pretoria, South Africa University of South Africa, Pretoria, South Africa https://orcid.org/0000-0002-6070-2632Search about this author , Gabriela Ochoa University of Stirling, Stirling, UK University of Stirling, Stirling, UK https://orcid.org/0000-0001-7649-5669Search about this author Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1416–1432https://doi.org/10.1145/3583133.3595051Published: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
Performance Analysis of Hybrid Sampling and Meta-heuristics

This paper investigates the effect of hybridising sampling algorithms with population-based meta-heuristics. Recent literature has shown that alternatives to the traditionally used pseudo-random number generators to generate the initial population of meta-heuristics can improve performance. However, most studies focus on sample sizes that are limited to the size of the initial populations. In contrast, this paper studies the effect of extended random initialisation, which uses relatively large samples and then initialises the meta-heuristics from the points in the sample with the best-found fitness values. A portfolio of three meta-heuristics, four sampling algorithms and three different sampling budgets are analysed from the fixed budget perspective on the BBOB benchmark suite. Statistical analysis of the results shows that the hybrid algorithms converge to better solutions than their non-hybrid counterparts. The results further indicate that large sample sizes can be used to generate landscape analysis features, ensuring reliable approximations of the investigated functions' properties without lessening the meta-heuristics' performance.
Coevolutionary Computation for Adversarial Deep Learning

tutorial Share on Coevolutionary Computation for Adversarial Deep Learning Authors: Jamal Toutouh University of Málaga, Málaga, Spain University of Málaga, Málaga, Spain https://orcid.org/0000-0003-1152-0346View Profile , Una-May O'Reilly Massachusetts Institute of Technology, Boston, USA Massachusetts Institute of Technology, Boston, USA https://orcid.org/0000-0001-6923-8445View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1379–1398https://doi.org/10.1145/3583133.3595049Published:24 July 2023Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks8 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
Deep BIAS: Detecting Structural Bias using Explainable AI

Evaluating the performance of heuristic optimisation algorithms is essential to determine how well they perform under various conditions. Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms. The toolbox can be used to identify biases in existing algorithms, as well as to test for bias in newly developed algorithms. In this article, we introduce a novel and explainable deep-learning expansion of the BIAS toolbox, called Deep-BIAS. Where the original toolbox uses 39 statistical tests and a Random Forest model to predict the existence and type of SB, the Deep-BIAS method uses a trained deep-learning model to immediately detect the strength and type of SB based on the raw performance distributions. Through a series of experiments with a variety of structurally biased scenarios, we demonstrate the effectiveness of Deep-BIAS. We also present the results of using the toolbox on 336 state-of-the-art optimisation algorithms, which showed the presence of various types of structural bias, particularly towards the centre of the objective space or exhibiting discretisation behaviour. The Deep-BIAS method outperforms the BIAS toolbox both in detecting bias and for classifying the type of SB. Furthermore, explanations can be derived using XAI techniques.
Evolution of Neural Networks

tutorial Open Access Share on Evolution of Neural Networks Author: Risto Miikkulainen The University of Texas at Austin and Cognizant AI Labs, Austin, USA The University of Texas at Austin and Cognizant AI Labs, Austin, USA https://orcid.org/0000-0002-0062-0037View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1008–1025https://doi.org/10.1145/3583133.3595036Published:24 July 2023Publication History 0citation139DownloadsMetricsTotal Citations0Total Downloads139Last 12 Months139Last 6 weeks24 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
Optimization Challenges at the European Space Agency

tutorial Share on Optimization Challenges at the European Space Agency Authors: Dario Izzo European Space Agency, Noordwijk, Netherlands European Space Agency, Noordwijk, Netherlands https://orcid.org/0000-0002-9846-8423View Profile , Manuel López-Ibáñez University of Manchester, Manchester, UK University of Manchester, Manchester, UK https://orcid.org/0000-0001-9974-1295View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1399–1415https://doi.org/10.1145/3583133.3595039Published:24 July 2023Publication History 0citation27DownloadsMetricsTotal Citations0Total Downloads27Last 12 Months27Last 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
Designing Convolutional Neural Networks using Surrogate assisted Genetic Algorithm for Medical Image Classification

Recently, Deep Learning (DL) algorithms have shown state-of-the-art performance in numerous tasks. The design of DL algorithms is time-consuming process that requires expert-level knowledge. To overcome this problem, Neural Architecture Search (NAS) is proposed, which automatically searches for the best neural network architecture for a given task. Indeed, evolutionary NAS has emerged as a widely studied research area, in which evolutionary algorithms are used to design neural networks. In this study, we proposed a novel encoding scheme to represent Convolutional Neural Network (CNN) architectures using continuous representation, which provides a larger search space and potentially finds better solutions compared to discrete representations. Moreover, it allows better exploitation of genetic operators, leading to a quick convergence of individuals. Furthermore, in order to reduce the computational time of the evaluation phase during the search process, surrogate models are used to provide an alternative objective function. To evaluate the effectiveness of the proposal, experiments are performed on CIFAR-10 and multiple datasets from the MedMNIST benchmark. The experimental results demonstrated the effectiveness of the proposed approach compared to several existing state-of-the-art algorithms.
Tag Affinity Criteria Influence Adaptive Evolution

This Hot-off-the-Press paper summarizes our recently published work, "Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity" [8]. This work appeared in Genetic Programming and Evolvable Machines. Genetic programming systems commonly use tag matching to decide interactions between system components. However, the implications of criteria used to determine affinity between tags with respect evolutionary dynamics have not been directly studied. We investigate differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. In experiments, we find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature.
LGP-VEC: A Vectorial Linear Genetic Programming for Symbolic Regression

Symbolic regression (SR) is a well-known regression problem, that aims to find a symbolic expression that best fits a given dataset. Linear Genetic Programming (LGP) is a good and powerful candidate for solving symbolic regression problems. However, current LGPs for SR only focus on finding scalar-valued functions, and limited work has been done on finding vector-valued functions with vectorial-based LGP. In addition, a comprehensive dataset for testing vectorial-based GP is still lacking in the literature. To this end, we propose a new extensive benchmark suite for vectorial symbolic regression. Furthermore, we propose a new vectorial LGP algorithm for symbolic regression, which directly deals with high dimensional data using vectorial representation and operations. Experimental results show that the proposed algorithm outperforms another recently published vectorial GP method on the benchmark suite for vector-valued functions and that it also generalizes better on unseen data.
Evolution Strategies with Seed Mirroring and End Tournament

This paper introduces two enhancements that apply to evolution strategies such as Augmented Random Search (ARS). These improvements target generalizable tasks with widely varying initial conditions, such as legged robot locomotion where the robot starts off in a random joint configuration. The first innovation builds upon the mirrored sampling feature of ARS. It mitigates the detrimental effect of unexplained variance on training stability by forcing the simulator to use the same random seed for both mirrored pairs. The second innovation is a multi-phase end tournament procedure performed right after the ARS method is complete. This tournament helps to ensure that the final product of training, a single model selected from the population, performs well over a wide range of random initial conditions. Improved results are demonstrated using MuJoCo simulations of legged robots.
Scalable Neuroevolution of Ensemble Learners

In recent years, machine learning has become increasingly important in daily life. One of the most popular machine learning models used in many applications is an Artificial Neural Network (ANN). While in applications such as automatic speech recognition there is sufficient knowledge about the expected behavior for each input to use supervised learning, other applications like robot control define only an overall target so that the expected output for a given input can be ambiguous, making supervised learning inapplicable. Therefore, Topology and Weight Evolving ANN (TWEANN) has been developed in the past to evolve ANN topologies and connection weights. However, challenges of TWEANN are the design of genetic recombination and the exploration of huge search spaces for suitable solutions induced in particular by large-scale problems which can lead to impractical runtimes.
Runtime Analysis of Population-based Evolutionary Algorithms - Part I: Steady State EAs

tutorial Open Access Share on Runtime Analysis of Population-based Evolutionary Algorithms - Part I: Steady State EAs Authors: Per Kristian Lehre University of Birmingham, Birmingham, UK University of Birmingham, Birmingham, UK https://orcid.org/0000-0002-9521-1251View Profile , Pietro Simone Oliveto University of Sheffield, Sheffield, UK Southern University of Science and Technology, Shenzhen, China University of Sheffield, Sheffield, UK Southern University of Science and Technology, Shenzhen, China https://orcid.org/0000-0001-8164-6767View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1271–1300https://doi.org/10.1145/3583133.3595056Published:24 July 2023Publication History 0citation23DownloadsMetricsTotal Citations0Total Downloads23Last 12 Months23Last 6 weeks17 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