2022 IEEE Congress on Evolutionary Computation (CEC)最新文献_第10页

A Comprehensive Analysis of the Invariance of Exploratory Landscape Analysis Features to Function Transformations 综合分析探索性景观分析特征对功能转换的不变性

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870313

Urban Škvorc, T. Eftimov, P. Korošec

{"title":"A Comprehensive Analysis of the Invariance of Exploratory Landscape Analysis Features to Function Transformations","authors":"Urban Škvorc, T. Eftimov, P. Korošec","doi":"10.1109/CEC55065.2022.9870313","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870313","url":null,"abstract":"Exploratory Landscape Analysis is a powerful technique that allows us to gain an understanding of a problem landscape solely by sampling the problem space. It has been successfully used in a number of applications, for example for the task of automatic algorithm selection. However, recent work has shown that Exploratory Landscape Analysis contains some specific weaknesses that its users should be aware of. As the technique is sample based, it has been shown to be sensitive to the choice of sampling strategy. Additionally, many landscape features are not invariant to transformations of the underlying samples which should have no effect on algorithm performance, specifically shifting and scaling. The analysis of the effect of shifting and scaling has so far only been demonstrated on a single problem set and dimensionality. In this paper, we perform a comprehensive analysis of the invariance of Exploratory Landscape Analysis features to these two transformations, by considering different sampling strate-gies, sampling sizes, problem dimensionalities, and benchmark problem sets to determine their individual and combined effect. We show that these factors have very limited influence on the features' invariance when they are considered either individually or combined.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127719701","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

A Study on Six Memetic Strategies for Multimodal Optimisation by Differential Evolution 基于差分进化的多模态优化六种模因策略研究

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870221

Ferrante Neri, Matthew Todd

引用次数: 1

Genetic Micro-Programs for Automated Software Testing with Large Path Coverage 大路径覆盖下自动化软件测试的遗传微程序

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870310

Jarrod Goschen, Anna Sergeevna Bosman, S. Gruner

引用次数: 0

Quantum Mating Operator: A New Approach to Evolve Chromosomes in Genetic Algorithms 量子配对算子:遗传算法中染色体进化的新方法

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870425

G. Acampora, Roberto Schiattarella, A. Vitiello

{"title":"Quantum Mating Operator: A New Approach to Evolve Chromosomes in Genetic Algorithms","authors":"G. Acampora, Roberto Schiattarella, A. Vitiello","doi":"10.1109/CEC55065.2022.9870425","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870425","url":null,"abstract":"Genetic Algorithms (GAs) are optimization methods that search near-optimal solutions by applying well-known operations such as selection, crossover and mutation. In particular, crossover and mutation are aimed at creating new solutions from selected parents with the goal of discovering better and better solutions in the search space. In literature, several approaches have been defined to create new solutions from the mating pool to try to improve the performance of genetic optimization. In this paper, the literature is enriched by introducing a new mating operator that harnesses the stochastic nature of quantum computation to evolve individuals in a classical genetic workflow. This new approach, named Quantum Mating Operator, acts as a multi-parent operator that identifies alleles' frequency patterns from a collection of individuals selected by means of conventional selection operators, and encodes them through a quantum state. This state is successively mutated and measured to generate a new classical chromosome. As shown by experimental results, GAs equipped with the proposed operator outperform those equipped with traditional crossover and mutation operators when used to solve well-known benchmark functions.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129430103","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: 3

Evolutionary Real-world Item Stock Allocation for Japanese Electric Commerce 日本电子商务的演化现实世界物品库存分配

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870390

Yasuyuki Mitsui, Y. Yamakoshi, Hiroyuki Sato

引用次数: 0

Ensemble of deep learning models with surrogate-based optimization for medical image segmentation 基于代理优化的深度学习模型集成医学图像分割

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870389

Truong Dang, Anh Vu Luong, Alan Wee-Chung Liew, J. Mccall, T. Nguyen

{"title":"Ensemble of deep learning models with surrogate-based optimization for medical image segmentation","authors":"Truong Dang, Anh Vu Luong, Alan Wee-Chung Liew, J. Mccall, T. Nguyen","doi":"10.1109/CEC55065.2022.9870389","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870389","url":null,"abstract":"Deep Neural Networks (DNNs) have created a breakthrough in medical image analysis in recent years. Because clinical applications of automated medical analysis are required to be reliable, robust and accurate, it is necessary to devise effective DNNs based models for medical applications. In this paper, we propose an ensemble framework of DNNs for the problem of medical image segmentation with a note that combining multiple models can obtain better results compared to each constituent one. We introduce an effective combining strategy for individual segmentation models based on swarm intelligence, which is a family of optimization algorithms inspired by biological processes. The problem of expensive computational time of the optimizer during the objective function evaluation is relieved by using a surrogate-based method. We train a surrogate on the objective function information of some populations and then use it to predict the objective values of each candidate in the subsequent populations. Experiments run on a number of public datasets indicate that our framework achieves competitive results within reasonable computation time.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128938142","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: 6

Quantum- Inspired Structure- Preserving Probabilistic Inference 量子启发结构-保持概率推断

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870260

Sascha Mücke, N. Piatkowski

{"title":"Quantum- Inspired Structure- Preserving Probabilistic Inference","authors":"Sascha Mücke, N. Piatkowski","doi":"10.1109/CEC55065.2022.9870260","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870260","url":null,"abstract":"Probabilistic methods serve as the underlying frame-work of various machine learning techniques. When using these models, a central problem is that of computing the partition function, whose computation is intractable for many models of interest. Here, we present the first quantum-inspired method that is especially designed for computing fast approximations to the partition function. Our approach uses a novel hardware solver for quadratic unconstrained binary optimization problems that relies on evolutionary computation. The specialized design allows us to assess millions of candidate solutions per second, leading to high quality maximum a-posterior (MAP) estimates, even for hard instances. We investigate the expected run-time of our solver and devise new ultra-sparse parity constraints to combine our device with the WISH approximation scheme. A SIMD-like packing strategy further allows us to solve multiple MAP instances at once, resulting in high efficiency and an additional speed-up. Numerical experiments show that our quantum-inspired approach produces accurate and robust results. While pure software implementations of the WISH algorithm typically run on large compute clusters with hundreds of CPUs, our results are achieved on two FPGA boards which both consume below 10 Watts. Moreover, our results extend seamlessly to adiabatic quantum computers.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121681649","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

An Enhanced Multi-Phase Stochastic Differential Evolution Framework for Numerical Optimization 一种改进的多阶段随机微分演化框架用于数值优化

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870438

Heba Abdelnabi, Mostafa Z. Ali, M. Daoud, R. Alazrai, A. Awajan, Robert Reynolds, P. N. Suganthan

{"title":"An Enhanced Multi-Phase Stochastic Differential Evolution Framework for Numerical Optimization","authors":"Heba Abdelnabi, Mostafa Z. Ali, M. Daoud, R. Alazrai, A. Awajan, Robert Reynolds, P. N. Suganthan","doi":"10.1109/CEC55065.2022.9870438","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870438","url":null,"abstract":"Real-life problems can be expressed as optimization problems. These problems pose a challenge for researchers to design efficient algorithms that are capable of finding optimal solutions with the least budget. Stochastic Fractal Search (SFS) proved its powerfulness as a metaheuristic algorithm through the large research body that used it to optimize different industrial and engineering tasks. Nevertheless, as with any meta-heuristic algorithm and according to the “No Free Lunch” theorem, SFS may suffer from immature convergence and local minima trap. Thus, to address these issues, a popular Differential Evolution variant called Success-History based Adaptive Differential Evolution (SHADE) is used to enhance SFS performance in a unique three-phase hybrid framework. Moreover, a local search is also incorporated into the proposed framework to refine the quality of the generated solution and accelerate the hybrid algorithm convergence speed. The proposed hybrid algorithm, namely eMpSDE, is tested against a diverse set of varying complexity optimization problems, consisting of well-known standard unconstrained unimodal and multimodal test functions and some constrained engineering design problems. Then, a comparative analysis of the performance of the proposed hybrid algorithm is carried out with the recent state of art algorithms to validate its competitivity.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124120942","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

Reducing the Number of Training Cases in Genetic Programming 减少遗传规划中训练案例的数量

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870327

Giacomo Zoppi, L. Vanneschi, M. Giacobini

引用次数: 0

Now I Know My Alpha, Beta, Gammas: Variants in an Epidemic Scheme 现在我知道我的Alpha, Beta, gamma:流行病计划中的变体

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870391

Michael Dubé, S. Houghten

引用次数: 3