2022 IEEE Congress on Evolutionary Computation (CEC)最新文献_第4页

Evolving Counterfactual Explanations with Particle Swarm Optimization and Differential Evolution 基于粒子群优化和差分进化的反事实解释演化

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870283

Hayden Andersen, Andrew Lensen, Will N. Browne, Yi Mei

引用次数: 1

Evolving Classification Rules for Predicting Hypoglycemia Events 预测低血糖事件的进化分类规则

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870380

Marina de la Cruz López, C. Cervigón, J. Alvarado, M. Botella, J. Hidalgo

引用次数: 1

Gabo: Gene Analysis Bitstring Optimization Gabo:基因分析位串优化

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870237

Jonatan Gómez, Elizabeth León Guzman

{"title":"Gabo: Gene Analysis Bitstring Optimization","authors":"Jonatan Gómez, Elizabeth León Guzman","doi":"10.1109/CEC55065.2022.9870237","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870237","url":null,"abstract":"This paper analyzes bitstring functions, character-izes genes according to their contribution to the genome's fitness, and proposes an optimization algorithm (G ABO) that uses this characterization for directing the optimization process. We define a gene's contribution as the difference between the genome's fitness when the gene takes a value of 1 and its fitness when the gene takes a value of 0. We characterize a gene as intron-like if it does not contribute to the genome's fitness (zero difference) and as separable-like if its contribution to the fitness of both the genome and genome's complement is the same. Gabo divides genes into two groups coding-like and intron-like genes. Then it searches for an optimal solution by reducing intron-like genes (IOSA) and analyzing coding-like genes (COSA). G Aborepeats these two steps while there are intron-like genes, not all genes are separable-like, and function evaluations are available. We test the performance of Gabo on well-known binary-encoding functions and a function that we define as the mix of them. Our results indicate that G Aboproduces the optimal or near to the optimal solution on the tested functions expending a reduced number of function evaluations and outperforming well-established optimization algorithms.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"364 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":"130960377","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 Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems 基于人工神经网络的超启发式自定义连续优化问题中基于群体的元启发式的初步研究

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870275

Jose M. Tapia-Avitia, J. M. Cruz-Duarte, I. Amaya, J. C. Ortíz-Bayliss, H. Terashima-Marín, N. Pillay

{"title":"A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems","authors":"Jose M. Tapia-Avitia, J. M. Cruz-Duarte, I. Amaya, J. C. Ortíz-Bayliss, H. Terashima-Marín, N. Pillay","doi":"10.1109/CEC55065.2022.9870275","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870275","url":null,"abstract":"Metaheuristics (MHs) are proven powerful algorithms for solving non-linear optimisation problems over discrete, continuous, or mixed domains. Applications have ranged from basic sciences to applied technologies. Nowadays, the literature contains plenty of MHs based on exceptional ideas, but often, they are just recombining elements from other techniques. An alternative approach is to follow a standard model that customises population-based MHs, utilising simple heuristics extracted from well-known MHs. Different approaches have explored the combination of such simple heuristics, generating excellent results compared to the generic MHs. Nevertheless, they present limitations due to the nature of the metaheuristic used to study the heuristic space. This work investigates a field of action for implementing a model that takes advantage of previously modified MHs by learning how to boost the performance of the tailoring process. Following this reasoning, we propose a hyper-heuristic model based on Artificial Neural Networks (ANNs) trained with processed sequences of heuristics to identify patterns that one can use to generate better MHs. We prove the feasibility of this model by comparing the results against generic MHs and other approaches that tailor unfolded MHs. Our results evidenced that the proposed model outperformed an average of 84 % of all scenarios; in particular, 89 % of basic and 77 % of unfolded approaches. Plus, we highlight the configurable capability of the proposed model, as it shows to be exceptionally versatile in regards to the computational budget, generating good results even with limited resources.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"37 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":"125359983","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

Genetic Programming Hyper-heuristic with Gaussian Process-based Reference Point Adaption for Many-Objective Job Shop Scheduling 基于高斯过程参考点自适应的遗传规划超启发式多目标作业车间调度

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870322

Atiya Masood, Gang Chen, Yi Mei, Harith Al-Sahaf, Mengjie Zhang

{"title":"Genetic Programming Hyper-heuristic with Gaussian Process-based Reference Point Adaption for Many-Objective Job Shop Scheduling","authors":"Atiya Masood, Gang Chen, Yi Mei, Harith Al-Sahaf, Mengjie Zhang","doi":"10.1109/CEC55065.2022.9870322","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870322","url":null,"abstract":"Job Shop Scheduling (JSS) is an important real-world problem. However, the problem is challenging because of many conflicting objectives and the complexity of production flows. Genetic programming-based hyper-heuristic (GP-HH) is a useful approach for automatically evolving effective dispatching rules for many-objective JSS. However, the evolved Pareto-front is highly irregular, seriously affecting the effectiveness of GP-HH. Although the reference points method is one of the most prominent and efficient methods for diversity maintenance in many-objective problems, it usually uses a uniform distribution of reference points which is only appropriate for a regular Pareto-front. In fact, some reference points may never be linked to any Pareto-optimal solutions, rendering them useless. These useless reference points can significantly impact the performance of any reference-point-based many-objective optimization algorithms such as NSGA-III. This paper proposes a new reference point adaption process that explicitly constructs the distribution model using Gaussian process to effectively reduce the number of useless reference points to a low level, enabling a close match between reference points and the distribution of Pareto-optimal solutions. We incorporate this mechanism into NSGA-III to build a new algorithm called MARP-NSGA-III which is compared experimentally to several popular many-objective algorithms. Experiment results on a large collection of many-objective benchmark JSS instances clearly show that MARP-NSGA-III can significantly improve the performance by using our Gaussian Process-based reference point adaptation mechanism.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"513 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":"122214010","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

XCSR with VAE using Gaussian Distribution Matching: From Point to Area Matching in Latent Space for Less-overlapped Rule Generation in Observation Space 基于高斯分布匹配的XCSR与VAE:从潜在空间的点到面积匹配到观测空间的少重叠规则生成

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870349

Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, K. Takadama

{"title":"XCSR with VAE using Gaussian Distribution Matching: From Point to Area Matching in Latent Space for Less-overlapped Rule Generation in Observation Space","authors":"Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, K. Takadama","doi":"10.1109/CEC55065.2022.9870349","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870349","url":null,"abstract":"This paper focuses on the matching mechanism of Learning Classifier System (LCS) in a continuous space and proposes a novel matching mechanism based on Gaussian distribution. This mechanism can match the “area” instead of the “point (one value)” in the continuous space unlike the conventional LCS such as XCSR (XCS with Continuous-Valued Inputs). Such an area matching contributes to generating the rules (called classifiers) with less-overlapped with other rules. Concretely, the proposed area matching mechanism employed in XCSR using VAE can generate appropriate classifiers for latent variables with high-dimensional inputs by VAE and create a human-interpretable observation space of human-interpretable classifiers. Since the latent variable in VAE is followed by Gaus-sian distribution, the following three matching mechanisms are compared: (i) the (single) point matching that selects the classifier which condition covers the mean of Gaussian distribution M; (ii) the multiple points matching that selects the classifier which condition covers the data sampled from Gaussian distribution (M, u); and (iii) the area matching that selects the classifier which condition roughly covers a certain area of Gaussian distribution (M, o). Through the intensive experiments on the high dimension maze problem, the following implications have been revealed: (1) the point matching in XCSR with VAE generates the ambiguous classifiers which conditions are overlapped with the other classifiers with the different action; (2) the sampling multiple points matching in XCSR with VAE has a potential of generating the less-overlapped classifiers by improving the data set through sampling. (3) the proposed area matching can generate the less-overlapped classifiers with the same learning steps, which corresponds to the time of the point matching.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"27 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":"125192152","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

Segregating Satellite Imagery Based on Soil Moisture Level Using Advanced Differential Evolutionary Multilevel Segmentation 基于土壤湿度水平的高级差分进化多级分割卫星图像

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870422

Meera Ramadas, A. Abraham

引用次数: 1

Metaheuristic Optimization Solving Demand Response Contract Markets with Network Validation 基于网络验证的需求响应合约市场的元启发式优化

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870325

Eduardo Lacerda, F. Lezama, J. Soares, B. Canizes, Z. Vale

{"title":"Metaheuristic Optimization Solving Demand Response Contract Markets with Network Validation","authors":"Eduardo Lacerda, F. Lezama, J. Soares, B. Canizes, Z. Vale","doi":"10.1109/CEC55065.2022.9870325","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870325","url":null,"abstract":"This article evaluates the performance of different metaheuristics (evolutionary algorithms) solving a cost mini-mization problem in demand response contract markets. The problem considers a contract market in which a distribution system operator (DSO) requests flexibility from aggregators with DR capabilities. We include a network validation approach in the evaluation of solutions, i.e., the DSO determines losses and voltage limit violations depending on the location of aggregators in the network. The validation of the network increases the complexity of the objective function since new network constraints are included in the formulation. Therefore, we advocate the use of metaheuristic optimization and a simulation procedure to overcome this issue. We compare different evolutionary algorithms, including the well-known differential evolution and other two more recent algorithms, the vortex search and the hybrid-adaptive differential evolution with decay function. Results demonstrate the effectiveness of these approaches in solving the proposed complex model under a realistic case study.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"43 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":"131623152","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 Initial Investigation of Data-Lean Transfer Evolutionary Optimization with Probabilistic Priors 基于概率先验的数据精益迁移进化优化初探

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870407

Ray Lim, Abhishek Gupta, Y. Ong

{"title":"An Initial Investigation of Data-Lean Transfer Evolutionary Optimization with Probabilistic Priors","authors":"Ray Lim, Abhishek Gupta, Y. Ong","doi":"10.1109/CEC55065.2022.9870407","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870407","url":null,"abstract":"Transfer evolutionary optimization (TrEO) has emerged as a computational paradigm to leverage related problem-solving information from various source tasks to boost convergence rates in a target task. State-of-the-art Tr EO algorithms have utilized a source-target similarity capture method with probabilistic priors that grants the ability to reduce negative transfers. A recent work makes use of an additional solution representation learning module to induce high ordinal correlation between source and target objective functions through source-to-target search space mappings, with the aim of promoting positive transfers between them. However, current implementations of this approach are found to be data-intensive - calling for all generated source data to be cached - leading to high storage costs in practice. As an alternative, this paper investigates the feasibility of a data-lean variant of the aforesaid approach, labeled as (1, G)-TrEO, in which only the first and final (Gth) generations of source data are used for solution representation learning and transfer. We conduct experimental analyses of (1, G)-TrEO using multi-objective benchmark functions as well as a practical example in vehicle crashworthiness design. Our results show that a simple data-lean transfer optimizer is able to achieve competitive performance. While this paper presents a first investigation of (1, G)-TrEO, we hope that the findings would inspire future forms of data-lean TrEO algorithms.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"23 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":"128039609","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

Global Search versus Local Search in Hyperparameter Optimization 超参数优化中的全局搜索与局部搜索

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870287

Yoshihiko Ozaki, Shintaro Takenaga, Masaki Onishi

引用次数: 1