Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference最新文献_第3页

A hierarchical clustering-based cooperative multi-population many-objective optimization algorithm 一种基于分层聚类的协同多种群多目标优化算法

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590476

Na Yang, Quanyuan Zhang, Ying Wu, Yisu Ge, Zhenzhou Tang

{"title":"A hierarchical clustering-based cooperative multi-population many-objective optimization algorithm","authors":"Na Yang, Quanyuan Zhang, Ying Wu, Yisu Ge, Zhenzhou Tang","doi":"10.1145/3583131.3590476","DOIUrl":"https://doi.org/10.1145/3583131.3590476","url":null,"abstract":"The increasing number of objectives poses a great challenge upon many-objective optimization algorithms (MaOOAs) when solving many-objective optimization problems (MaOOPs), since it is rather difficult to obtain well-distributed solutions with tight convergence. To efficiently improve the ability of solving MaOOPs, this paper proposes a hierarchical clustering-based cooperative multi-population many-objective optimization algorithm (C2MP-MaOOA). Specifically, a hierarchical clustering-based population division strategy is proposed in C2MP-MaOOA, which is able to effectively optimize different regions of the Pareto front (PF) regardless of its shape, so as to maintain population diversity and accelerate convergence. Any single-objective optimizer can be applied in C2MP-MaOOA to optimize a subpopulation. To comprehensively evaluate the performance of C2MP-MaOOA, it was compared with eight state-of-the-art existing algorithms and two variants of C2MP-MaOOA on 63 MaOOPs selected from DTLZ, MaF, and WFG benchmark suites. The results indicate that C2MP-MaOOA has the best overall performance for each benchmark suite, which demonstrates that C2MP-MaOOA is quite competitive in solving MaOOPs.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91156883","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

Vertical-Axis Wind Turbine Design Using Surrogate-assisted Optimization with Physical Experiments In-loop 物理实验在环的代理辅助优化垂直轴风力机设计

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590462

Matthew Lette, Kamrul Hasan Rahi, H. Singh, T. Ray

{"title":"Vertical-Axis Wind Turbine Design Using Surrogate-assisted Optimization with Physical Experiments In-loop","authors":"Matthew Lette, Kamrul Hasan Rahi, H. Singh, T. Ray","doi":"10.1145/3583131.3590462","DOIUrl":"https://doi.org/10.1145/3583131.3590462","url":null,"abstract":"Most of the existing wind power comes from the use of traditional Horizontal-Axis Wind Turbines (HAWT), which typically require installation over vast countryside areas (in wind farms) due to considerations such as wake effects and noise. Recently, there has been an increasing interest to explore an alternative solution - Vertical-Axis Wind Turbines (VAWT) - that may be more suitable for compact urban areas. In this paper, we conduct design optimization of twin-blade VAWTs by evaluating the candidate designs through direct small-scale prototyping and physical experiments in-loop. The problem is a practical example of expensive optimization where the number of evaluations affordable are severely limited. In addition to the conventional single-objective form (maximize rotational speed), we also solve a multi-objective version of the problem (maximize rotational speed and minimize mass). For conducting optimization, we leverage surrogate-assisted approaches that make use of both predicted mean and uncertainties in modeling for an efficient exploration of the design space. The experiments demonstrate that the approach is able to generate non-intuitive designs that are competitive or better than the baseline (classic twin-blade Savonius design) within a small evaluation budget. The study also strengthens the case for applying this approach for design optimization in general.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79339982","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

STHV-Net: Hypervolume Approximation based on Set Transformer STHV-Net:基于集合变压器的超体积逼近

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590378

Hana Zhu, Ke Shang, H. Ishibuchi

引用次数: 0

Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) Classification 可解释重度抑郁障碍(MDD)分类的有效EEG特征选择

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590398

Vojtěch Mrázek, Soyiba Jawed, Muhammad Arif, A. Malik

{"title":"Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) Classification","authors":"Vojtěch Mrázek, Soyiba Jawed, Muhammad Arif, A. Malik","doi":"10.1145/3583131.3590398","DOIUrl":"https://doi.org/10.1145/3583131.3590398","url":null,"abstract":"In this paper, we propose an interpretable electroencephalogram (EEG)-based solution for the diagnostics of major depressive disorder (MDD). The acquisition of EEG experimental data involved 32 MDD patients and 29 healthy controls. A feature matrix is constructed involving frequency decomposition of EEG data based on power spectrum density (PSD) using the Welch method. Those PSD features were selected, which were statistically significant. To improve interpretability, the best features are first selected from feature space via the non-dominated sorting genetic (NSGA-II) evolutionary algorithm. The best features are utilized for support vector machine (SVM), and k-nearest neighbors (k-NN) classifiers, and the results are then correlated with features to improve the interpretability. The results show that the features (gamma bands) extracted from the left temporal brain regions can distinguish MDD patients from control significantly. The proposed best solution by NSGA-II gives an average sensitivity of 93.3%, specificity of 93.4% and accuracy of 93.5%. The complete framework is published as open-source at https://github.com/ehw-fit/eeg-mdd.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74327164","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

Comparing Metaheuristic Optimization Algorithms for Ambulance Allocation: An Experimental Simulation Study 比较救护车分配的元启发式优化算法:一个实验模拟研究

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590345

Magnus Eide Schjølberg, Nicklas Paus Bekkevold, Xavier Sánchez-Díaz, O. Mengshoel

{"title":"Comparing Metaheuristic Optimization Algorithms for Ambulance Allocation: An Experimental Simulation Study","authors":"Magnus Eide Schjølberg, Nicklas Paus Bekkevold, Xavier Sánchez-Díaz, O. Mengshoel","doi":"10.1145/3583131.3590345","DOIUrl":"https://doi.org/10.1145/3583131.3590345","url":null,"abstract":"The optimization of Emergency Medical Services is a central issue in modern healthcare systems. With this in focus, we study a data set containing medical emergencies for the years 2015--2019 from Oslo and Akershus, Norway. By developing a discrete trace-based simulation model based on the data set, we compute average response times that are used to optimize ambulance allocations to stations in the region. We study several metaheuristics, specifically genetic, stochastic local search, and memetic algorithms. These metaheuristics are tested using the simulation to optimize ambulance allocations, considering response times. The algorithms are compared against each other and a set of baseline allocation models over different time periods. The main results of our experimental simulation study are that: (i) the metaheuristics generally outperform the simpler baselines, (ii) the best-performing metaheuristic is the genetic algorithm, and (iii) the performance difference between the metaheuristics and the simpler baselines increases in situations with high demand on ambulances. Finally, we present suggestions for future work that may help to further improve upon the current state-of-the-art.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76587753","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

Optimizing Dispatching Strategies for Semiconductor Manufacturing Facilities with Genetic Programming 基于遗传规划的半导体生产设施调度优化策略

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590402

Benjamin Kovács, Pierre Tassel, M. Gebser

{"title":"Optimizing Dispatching Strategies for Semiconductor Manufacturing Facilities with Genetic Programming","authors":"Benjamin Kovács, Pierre Tassel, M. Gebser","doi":"10.1145/3583131.3590402","DOIUrl":"https://doi.org/10.1145/3583131.3590402","url":null,"abstract":"Optimizing operations in semiconductor manufacturing facilities is challenging. The production line can be modeled as an NP-hard constrained flexible job-shop scheduling problem, intractable with mathematical optimization due to its scale. Therefore, decision-making in factories is dominated by handcrafted heuristics. Though machine learning-based approaches proved efficient in solving such problems, their applications are limited due to the lack of trust in the underlying black-box models, and issues with scalability for larger instances. This work presents a genetic programming-based method to generate explainable, improved dispatching heuristics. Our method outputs a set of human-readable dispatching strategies, verifiable by scheduling experts before deployment. In case of minor changes in the environment or the optimization objectives, the continued evolution of the candidate solutions is possible without starting the training process from scratch. The introduced method is evaluated on a simulator executing real-world scale instances. The resulting heuristics improve the key performance indicators of the generated schedules. Furthermore, the generated dispatchers are easy to integrate into existing industrial systems. These favorable properties make the method applicable to various large-scale, dynamic, practical scheduling scenarios, where adaptions to different environments go along with modest human effort limited to the design of a fitness function.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78145225","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 General Purpose Representation and Adaptive EA for Evolving Graphs 演化图的通用表示和自适应EA

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590431

Eric Medvet, Simone Pozzi, L. Manzoni

{"title":"A General Purpose Representation and Adaptive EA for Evolving Graphs","authors":"Eric Medvet, Simone Pozzi, L. Manzoni","doi":"10.1145/3583131.3590431","DOIUrl":"https://doi.org/10.1145/3583131.3590431","url":null,"abstract":"Graphs are a way to describe complex entities and their relations that apply to many practically relevant domains. However, domains often differ not only in the properties of nodes and edges, but also in the constraints imposed to the overall structure. This makes hard to define a general representation and genetic operators for graphs that permit the evolutionary optimization over many domains. In this paper, we tackle this challenge. We first propose a representation template that can be customized by users for specific domains: the constraints and the genetic operators are given in Prolog, a declarative programming language for operating with logic. Then, we define an adaptive evolutionary algorithm that can work with a large number of genetic operators by modifying their usage probability during the evolution: in this way, we relieve the user from the burden of selecting in advance only operators that are \"good enough\". We experimentally evaluate our proposal on two radically different domains to demonstrate its applicability and effectiveness: symbolic regression with trees and text extraction with finite-state automata. The results are promising: our approach does not trade effectiveness for versatility and is not worse than other domain-tailored approaches.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81377711","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

Probabilistic model with evolutionary optimization for cognitive diagnosis 基于进化优化的认知诊断概率模型

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590522

Chenyang Bu, Zhiyong Cao, Chenlong He, Yuhong Zhang

{"title":"Probabilistic model with evolutionary optimization for cognitive diagnosis","authors":"Chenyang Bu, Zhiyong Cao, Chenlong He, Yuhong Zhang","doi":"10.1145/3583131.3590522","DOIUrl":"https://doi.org/10.1145/3583131.3590522","url":null,"abstract":"Cognitive Diagnostic Models (CDMs) aim to analyze students' cognitive levels of each knowledge component (KC) by mining educational data. Existing CDMs can be mainly divided into two categories, i.e., traditional probability-based and neural-network-based. Most probabilistic models have the advantages of simplicity and good interpretability, but suffer from slow training time in the case of a large number of KCs. Neural-network-based methods are widely considered to be superior to probabilistic models due to their good performance. However, neural network methods are less interpretable than probabilistic models, thus limiting their usefulness in practice. Because most existing probabilistic models are optimized iteratively based on single-point-based search methods, they may be easily trapped in local optimum due to the influence of the initial points. And evolutionary algorithms (EAs) have good global search ability. Therefore, an interesting question is whether a simple probabilistic model based on evolutionary optimization can rival neural-network in limited optimization time. Thus, a hybrid EA with a customized local search is proposed. Experimental results on three real-world datasets show that our method outperforms the compared 7 models (including 2 state-of-the-art neural-network-based models); and the running time of our method is significantly less than the compared probabilistic models.","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":"87114910","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

Pareto Local Search is Competitive with Evolutionary Algorithms for Multi-Objective Neural Architecture Search Pareto局部搜索与进化算法在多目标神经结构搜索中的竞争

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590395

Q. Phan, N. H. Luong

{"title":"Pareto Local Search is Competitive with Evolutionary Algorithms for Multi-Objective Neural Architecture Search","authors":"Q. Phan, N. H. Luong","doi":"10.1145/3583131.3590395","DOIUrl":"https://doi.org/10.1145/3583131.3590395","url":null,"abstract":"Neural architecture search (NAS) involves automatically searching for promising deep neural network structures in certain architecture spaces. Depending on the number of criteria being concerned, NAS can be formulated as single-objective optimization problems (SONAS) or multi-objective optimization problems (MONAS). Evolutionary algorithms (EAs) are common approaches for NAS due to their effectiveness in solving challenging combinatorial problems. Recent studies, however, have analyzed SONAS landscapes and indicated that while NAS problems are multi-modal but local search algorithms with simple perturbation operators can escape local optima to reach global optima without much difficulty. Such investigations for MONAS remain under-explored. In this paper, we employ local optimal networks (LONs) for visually structuring the MONAS landscape with a simple local search procedure. Via detailed analyses, we then design LOMONAS, a dedicated Pareto local search algorithm for MONAS. The experimental results on four NAS benchmarks (MacroNAS, NAS-Bench-101, NAS-Bench-201, and NAS-Bench-ASR) exhibit the superior performance of LOMONAS compared to two widely-used multi-objective EAs (MOEAs), NSGA-II and MOEA/D. The findings indicate that Pareto local search algorithms are competitive with MOEAs in solving MONAS problems.","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":"86445863","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

How the Morphology Encoding Influences the Learning Ability in Body-Brain Co-Optimization 形态学编码对体脑协同优化学习能力的影响

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590429

F. Pigozzi, Federico Julian Camerota Verdù, E. Medvet

{"title":"How the Morphology Encoding Influences the Learning Ability in Body-Brain Co-Optimization","authors":"F. Pigozzi, Federico Julian Camerota Verdù, E. Medvet","doi":"10.1145/3583131.3590429","DOIUrl":"https://doi.org/10.1145/3583131.3590429","url":null,"abstract":"Embedding the learning of controllers within the evolution of morphologies has emerged as an effective strategy for the co-optimization of agents' bodies and brains. Intuitively, that is how nature shaped animal life on Earth. Still, the design of such co-optimization is a complex endeavor; one issue is the choice of the genetic encoding for the morphology. Such choice can be crucial for the effectiveness of learning, i.e., how fast and to what degree agents adapt, through learning, during their life. Here we evolve the morphologies of voxel-based soft agents with two different encodings, direct and indirect while learning the controllers with reinforcement learning. We experiment with three tasks, ranging from cave crawling to beam toppling, and study how the encoding influences the learning outcome. Our results show that the direct encoding corresponds to increased ability to learn, mostly in terms of learning speed. The same is not always true for the indirect one. We link these results to different shades of the Baldwin effect, consisting of morphologies being selected for increasing an agent's ability to learn during its lifetime.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89137359","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: 2