Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference最新文献_第7页

Swarms of Artificial Platelets for Emergent Hole Detection and Healing in Wireless Sensor Networks 无线传感器网络中用于紧急空洞检测和修复的人工血小板群

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590468

Giada Simionato, Federico A. Galatolo, M. Cimino

{"title":"Swarms of Artificial Platelets for Emergent Hole Detection and Healing in Wireless Sensor Networks","authors":"Giada Simionato, Federico A. Galatolo, M. Cimino","doi":"10.1145/3583131.3590468","DOIUrl":"https://doi.org/10.1145/3583131.3590468","url":null,"abstract":"Most of the applications of wireless sensor networks require the continuous coverage of a region of interest. The irregular deployment of the nodes, or their failure, could result in holes in the coverage, thus jeopardizing such requirement. Methods to recover the sensing capabilities usually demand the availability of redundant full-fledged nodes, whose relocation should heal the holes. These solutions, however, do not consider the high cost of obtaining redundant, typically complex, devices, nor that they could in turn fail. In this work, we propose a bio-inspired and emergent approach toward hole detection and healing using a swarm of resource-constrained agents with reduced sensing capabilities, whose behavior draws inspiration from the concepts underlying blood coagulation. The swarm follows three rules: activation, adhesion, and cohesion, adapted from the behavior exhibited by platelets during the human healing process. Relying only on local and relative information, the mobile agents can detect the holes border and place themselves in locally optimal positions to temporarily restore the service. To validate the algorithm, we have developed a distributed, multi-process simulator. Experimental results show that the proposed method efficiently detects and heals the holes, outperforming two state-of-the-art solutions. It also demonstrates good robustness and flexibility to agent failure.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86465551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Effective One-Shot Neural Architecture Search Method with Supernet Fine-Tuning for Image Classification 一种有效的基于超网络微调的一次性神经结构搜索图像分类方法

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590438

Gonglin Yuan, Bing Xue, Mengjie Zhang

{"title":"An Effective One-Shot Neural Architecture Search Method with Supernet Fine-Tuning for Image Classification","authors":"Gonglin Yuan, Bing Xue, Mengjie Zhang","doi":"10.1145/3583131.3590438","DOIUrl":"https://doi.org/10.1145/3583131.3590438","url":null,"abstract":"Neural architecture search (NAS) is becoming increasingly popular for its ability to automatically search for an appropriate network architecture, avoiding laborious manual designing processes, and potentially introducing novel structures. However, many NAS methods suffer from heavy computational consumption. One-shot NAS alleviates this issue by training a big supernet and allowing all the candidates to inherit weights from the supernet, avoiding training from scratch. However, the performance evaluations in one-shot methods might not always be reliable due to the weight co-adaption issue inside the supernet. This paper proposes a super-net fine-tuning strategy to allow the supernet's weights to adapt to the new focused search region along with the evolutionary process. Furthermore, a new genetic algorithm-based search method is designed to offer an effective path-sampling strategy in the search region and provide a new population generation method to preclude unfair fitness comparisons between different populations. The experimental results demonstrate the proposed method achieves promising results compared with 32 peer competitors in terms of the algorithm's computational cost and the searched architecture's performance. Specifically, the proposed method achieves classification error rates of 2.50% and 17.07% within only 0.50 and 0.92 GPU-days on CIFAR10 and CIFAR100, respectively.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86277915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-adaptation Can Help Evolutionary Algorithms Track Dynamic Optima 自适应有助于进化算法跟踪动态最优

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590494

P. Lehre, Xiaoyu Qin

{"title":"Self-adaptation Can Help Evolutionary Algorithms Track Dynamic Optima","authors":"P. Lehre, Xiaoyu Qin","doi":"10.1145/3583131.3590494","DOIUrl":"https://doi.org/10.1145/3583131.3590494","url":null,"abstract":"Real-world optimisation problems often involve dynamics, where objective functions may change over time. Previous studies have shown that evolutionary algorithms (EAs) can solve dynamic optimisation problems. Additionally, the use of diversity mechanisms, populations, and parallelisation can enhance the performance of EAs in dynamic environments if appropriate parameter settings are utilised. Self-adaptation, which encodes parameters in genotypes of individuals and allows them to evolve together with solutions, can help configure parameters of EAs. This parameter control mechanism has been proved to effectively handle a static problem with unknown structure. However, the benefit of self-adaptation on dynamic optimisation problems remains unknown. We consider a tracking dynamic optima problem, the so-called Dynamic Substring Matching (DSM) problem, which requires algorithms to successively track a sequence of structure-changing optima. Our analyses show that mutation-based EAs with a fixed mutation rate have a negligible chance of tracking these dynamic optima, while the self-adaptive EA tracks them with an overwhelmingly high probability. Furthermore, we provide a level-based theorem with tail bounds, which bounds the chance of the algorithm finding the current optima within a given evaluation budget. Overall, self-adaptation is promising for tracking dynamic optima.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83455080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent Search 神经进化分歧搜索中的可进化性和行为景观

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590427

Bruno Gašperov, Marko Đurasević

{"title":"On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent Search","authors":"Bruno Gašperov, Marko Đurasević","doi":"10.1145/3583131.3590427","DOIUrl":"https://doi.org/10.1145/3583131.3590427","url":null,"abstract":"Evolvability refers to the ability of an individual genotype (solution) to produce offspring with mutually diverse phenotypes. Recent research has demonstrated that divergent search methods, particularly novelty search, promote evolvability by implicitly creating selective pressure for it. The main objective of this paper is to provide a novel perspective on the relationship between neuroevolutionary divergent search and evolvability. In order to achieve this, several types of walks from the literature on fitness landscape analysis are first adapted to this context. Subsequently, the interplay between neuroevolutionary divergent search and evolvability under varying amounts of evolutionary pressure and under different diversity metrics is investigated. To this end, experiments are performed on Fetch Pick and Place, a robotic arm task. Moreover, the performed study in particular sheds light on the structure of the genotype-phenotype mapping (the behavior landscape). Finally, a novel definition of evolvability that takes into account the evolvability of offspring and is appropriate for use with discretized behavior spaces is proposed, together with a Markov-chain-based estimation method for it.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73401602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fourier Transform-based Surrogates for Permutation Problems 基于傅立叶变换的置换问题替代物

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590425

F. Chicano, B. Derbel, S. Vérel

{"title":"Fourier Transform-based Surrogates for Permutation Problems","authors":"F. Chicano, B. Derbel, S. Vérel","doi":"10.1145/3583131.3590425","DOIUrl":"https://doi.org/10.1145/3583131.3590425","url":null,"abstract":"In the context of pseudo-Boolean optimization, surrogate functions based on the Walsh-Hadamard transform have been recently proposed with great success. It has been shown that lower-order components of the Walsh-Hadamard transform have usually a larger influence on the value of the objective function. Thus, creating a surrogate model using the lower-order components of the transform can provide a good approximation to the objective function. The Walsh-Hadamard transform in pseudo-Boolean optimization is a particularization in the binary representation of a Fourier transform over a finite group, precisely defined in the framework of group representation theory. Using this more general definition, it is possible to define a Fourier transform for the functions over permutations. We propose in this paper the use of surrogate functions based on the Fourier transforms over the permutation space. We check how similar the proposed surrogate models are to the original objective function and we also apply regression to learn a surrogate model based on the Fourier transform. The experimental setting includes two permutation problems for which the exact Fourier transform is unknown based on the problem parameters: the Asteroid Routing Problem and the Single Machine Total Weighted Tardiness.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74618297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EA-based ASV Trajectory Planner for Detecting Cyanobacterial Blooms in Freshwater 基于ea的淡水蓝藻华检测ASV轨迹规划器

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590484

Gonzalo Carazo-Barbero, E. Besada-Portas, J. L. Risco-Martín, J. A. López-Orozco

{"title":"EA-based ASV Trajectory Planner for Detecting Cyanobacterial Blooms in Freshwater","authors":"Gonzalo Carazo-Barbero, E. Besada-Portas, J. L. Risco-Martín, J. A. López-Orozco","doi":"10.1145/3583131.3590484","DOIUrl":"https://doi.org/10.1145/3583131.3590484","url":null,"abstract":"Cyanobacterial Blooms (CBs) constitute a relevant ecological and public health problem since they often produce toxic metabolites that endanger the lives of many species, and they prevent human water consumption and recreational use. To determine the locations of CBs in lentic water bodies, we present a new planner based on Evolutionary Algorithms (EAs) that optimizes the trajectory of an Autonomous Surface Vehicle (ASV) equipped with a probe capable of detecting CBs. The planner 1) exploits the information provided by a particle transport simulator that determines the CB distribution from the water currents and the inherent CB behavior (in particular, its biological growth and vertical displacements) and 2) is supported by an EA that optimizes the mission duration, the ASV trajectory length, and the contributions of each simulated particle to the predicted cyanobacterial concentration along the ASV trajectory. The planner also ensures the trajectory feasibility from the ASV, probe, and water body perspective; and refines the trajectory shape by increasing the number of the decision variables during the iteration of an EA supported by usual NSGA-II operations. The results over different scenarios show that the planner determines overall good solutions that adapt the ASV trajectory to the evolution of CB distribution.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75060386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Searching for Quality: Genetic Algorithms and Metamorphic Testing for Software Engineering ML 搜索质量:用于软件工程的遗传算法和变形测试

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590379

Leonhard Applis, Annibale Panichella, Ruben Marang

{"title":"Searching for Quality: Genetic Algorithms and Metamorphic Testing for Software Engineering ML","authors":"Leonhard Applis, Annibale Panichella, Ruben Marang","doi":"10.1145/3583131.3590379","DOIUrl":"https://doi.org/10.1145/3583131.3590379","url":null,"abstract":"More machine learning (ML) models are introduced to the field of Software Engineering (SE) and reached a stage of maturity to be considered for real-world use; But the real world is complex, and testing these models lacks often in explainability, feasibility and computational capacities. Existing research introduced meta-morphic testing to gain additional insights and certainty about the model, by applying semantic-preserving changes to input-data while observing model-output. As this is currently done at random places, it can lead to potentially unrealistic datapoints and high computational costs. With this work, we introduce genetic search as an aid for metamorphic testing in SE ML. Exploiting the delta in output as a fitness function, the evolutionary intelligence optimizes the transformations to produce higher deltas with less changes. We perform a case study minimizing F1 and MRR for Code2Vec on a representative sample from java-small with both genetic and random search. Our results show that within the same amount of time, genetic search was able to achieve a decrease of 10% in F1 while random search produced 3% drop.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80792733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Hyper-Heuristic to Generate Heuristic-based Adaptive Sliding Mode Controller Tuners for Buck-Boost Converters 自动超启发式生成基于启发式的Buck-Boost变换器自适应滑模控制器调谐器

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590510

Daniel F. Zambrano-Gutierrez, J. M. Cruz-Duarte, H. Castañeda

{"title":"Automatic Hyper-Heuristic to Generate Heuristic-based Adaptive Sliding Mode Controller Tuners for Buck-Boost Converters","authors":"Daniel F. Zambrano-Gutierrez, J. M. Cruz-Duarte, H. Castañeda","doi":"10.1145/3583131.3590510","DOIUrl":"https://doi.org/10.1145/3583131.3590510","url":null,"abstract":"Metaheuristics are commonly used to solve complex and challenging problems, particularly in electrical system applications. Nevertheless, there is a colorful palette of metaheuristics to select from, which can be overwhelming for a practitioner with solid experience in controlling electrical systems. Still, it is well-known that empirical or analytical tuning of controllers is no trivial labor. This work implements a methodology to address the Metaheuristic Composition Optimization Problem to coin a heuristic-based technique that guides an initially random population of individuals to find the optimal set of parameters of an Adaptive Sliding Mode Controller in the search space. As a case study, we implement this methodology for controlling the dynamic response of a DC-DC Buck-Boost converter under different conditions, including nonlinear perturbations. The objective is clear: find the optimal heuristic-based solver to determine the control parameters satisfying a predefined performance. Our experimental results reveal the reliability and potential of the proposed methodology when finding suitable solutions for control system design applications. We also support our findings with statistical analyses performed on the results obtained by the tailored metaheuristic against other classical heuristic-based methods.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78142984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Investigation of Geometric Semantic GP with Linear Scaling 具有线性标度的几何语义GP研究

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590418

Giorgia Nadizar, Fraser Garrow, Berfin Sakallioglu, Lorenzo Canonne, Sara Silva, L. Vanneschi

{"title":"An Investigation of Geometric Semantic GP with Linear Scaling","authors":"Giorgia Nadizar, Fraser Garrow, Berfin Sakallioglu, Lorenzo Canonne, Sara Silva, L. Vanneschi","doi":"10.1145/3583131.3590418","DOIUrl":"https://doi.org/10.1145/3583131.3590418","url":null,"abstract":"Geometric semantic genetic programming (GSGP) and linear scaling (LS) have both, independently, shown the ability to outperform standard genetic programming (GP) for symbolic regression. GSGP uses geometric semantic genetic operators, different from the standard ones, without altering the fitness, while LS modifies the fitness without altering the genetic operators. So far, these two methods have already been joined together in only one practical application. However, to the best of our knowledge, a methodological study on the pros and cons of integrating these two methods has never been performed. In this paper, we present a study of GSGP-LS, a system that integrates GSGP and LS. The results, obtained on five hand-tailored benchmarks and six real-life problems, indicate that GSGP-LS outperforms GSGP in the majority of the cases, confirming the expected benefit of this integration. However, for some particularly hard datasets, GSGP-LS overfits training data, being outperformed by GSGP on unseen data. Additional experiments using standard GP, with and without LS, confirm this trend also when standard crossover and mutation are employed. This contradicts the idea that LS is always beneficial for GP, warning the practitioners about its risk of overfitting in some specific cases.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81635987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A MILP-Based Very Large-Scale Neighborhood Search for the Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery 同时取货的异构车辆路径问题的基于milp的大规模邻域搜索

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3591054

Napoleão Nepomuceno, Ricardo Saboia, André Coelho

引用次数: 0