Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference最新文献_第8页

Co-operative Co-evolutionary Many-objective Embedded Multi-label Feature Selection with Decomposition-based PSO 基于分解粒子群的协同进化多目标嵌入式多标签特征选择

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590373

Kaan Demir, B. Nguyen, Bing Xue, Mengjie Zhang

{"title":"Co-operative Co-evolutionary Many-objective Embedded Multi-label Feature Selection with Decomposition-based PSO","authors":"Kaan Demir, B. Nguyen, Bing Xue, Mengjie Zhang","doi":"10.1145/3583131.3590373","DOIUrl":"https://doi.org/10.1145/3583131.3590373","url":null,"abstract":"Multi-label classification is an emerging machine-learning problem involving the prediction of a set of class labels based on the instance's features. In real-world problems, there are hundreds or thousands of features, many of which are irrelevant or redundant, resulting in a large search space for feature selection, i.e. to find small and discriminate feature subsets that improve the classification performance. Feature selection for multi-label classification is a many-objective optimisation problem with more than three main conflicting objectives: one of which is to reduce the number of selected features. There are many metrics for measuring multi-label classification performance, each of which can conflict with one another depending on the task. Hence, multi-label feature selection is a many-objective optimisation problem when three or more classification metrics and the number of selected features are optimised. In this paper, we propose to combine multi-label feature selection with evolutionary many-objective optimisation to address the above challenges and handle the trade-offs between multiple classification metrics and the number of selected features, using a decomposition-based algorithm. The results demonstrate that our proposed method is capable of finding discriminative and small feature subsets that can significantly improve the classification performances in comparison with other many-objective feature selection approaches.","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":"87383047","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

Evolutionary Computation Evolving 进化计算

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3600058

K. De Jong

引用次数: 0

Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS 基于XCS降维的潜在空间间接探索高维规则

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590439

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

{"title":"Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS","authors":"Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, K. Takadama","doi":"10.1145/3583131.3590439","DOIUrl":"https://doi.org/10.1145/3583131.3590439","url":null,"abstract":"To mine high-dimensional rules in Learning Classifier Systems (LCSs) through a reduction of the dimensionality of input data, this paper proposes a novel approach that indirectly learns the rules in the \"latent space\" based on the rewards of the reconstructed rules in the \"observation space\". We call this approach Learning Strategy by exploring rules in Observation space via Latent space (LS-OvL), which is based on two rule representations in the observation and latent space. Concretely, LS-OvL explores the rules by searching the latent space as the reduced dimensional input space by an autoencoder and evaluates them in the observation space by reconstructing them from the latent space. Such a design is significant because it prevents the generation of inaccurate rules during the reconstitution process from the latent space to the observation space. Through a comparison LS-OvL with the conventional learning strategy, which explores and evaluates the rules in the only latent space and reconstructs them in the observation space, the experimental results show that (1) LS-OvL outperforms the conventional learning strategy in terms of the acquired reward and the population size, and (2) LS-OvL can generate explainable and classifiable high-dimensional rules.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89821850","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

Channel Configuration for Neural Architecture: Insights from the Search Space 神经结构的通道配置:来自搜索空间的见解

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590386

S. Thomson, G. Ochoa, Nadarajen Veerapen, Krzysztof Michalak

引用次数: 0

Symbolic Regression Trees as Embedded Representations 作为嵌入表示的符号回归树

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590423

Victor Caetano, Matheus Cândido Teixeira, G. Pappa

{"title":"Symbolic Regression Trees as Embedded Representations","authors":"Victor Caetano, Matheus Cândido Teixeira, G. Pappa","doi":"10.1145/3583131.3590423","DOIUrl":"https://doi.org/10.1145/3583131.3590423","url":null,"abstract":"Representation learning is an area responsible for learning data representations that makes it easier for machine learning algorithms to extract useful information from them. Deep learning currently has the most effective methods for this task and can learn distributed representations - also known as embeddings - able to represent different properties of the data and their relationship. In this direction, this paper introduces a new way to look at tree-like GP individuals for symbolic regression. Given a set of predefined operators and a sufficiently large number of solutions sampled from the space, we train a transformer to learn an encoding/decoding function. By transforming a tree representation into a distributed representation, we are able to measure distances between trees in a much more efficient way and, more importantly, generate the potential for these representations to capture semantics. We show the distance accounting for embedding presents results very similar to those of a tree-edition, which reflects their syntactic similarity. Although the model as it stands is not able to capture semantics yet, we show its potential by using the generated tree-representation model in a simple task: measuring distances between trees in a fitness-sharing scenario.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83778776","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

Guiding the Exploration of the Solution Space in Walking Robots Through Growth-Based Morphological Development 通过基于生长的形态发展指导步行机器人解空间的探索

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590489

M. Naya-Varela, A. Faíña, R. Duro

{"title":"Guiding the Exploration of the Solution Space in Walking Robots Through Growth-Based Morphological Development","authors":"M. Naya-Varela, A. Faíña, R. Duro","doi":"10.1145/3583131.3590489","DOIUrl":"https://doi.org/10.1145/3583131.3590489","url":null,"abstract":"In human beings, the joint development of the body and cognitive system has been shown to facilitate the acquisition of new skills and abilities. In the literature, these natural principles have been applied to robotics with mixed results and different authors have suggested several hypotheses to explain them. One of the most popular hypotheses states that morphological development improves learning by increasing exploration of the solution space, avoiding stagnation in local optima. In this article, we are going to study the influence of growth-based morphological development and its nuances as a tool to improve the exploration of the solution space. We will perform a series of experiments over two different robot morphologies which learn to walk. Furthermore, we will compare these results to another optimization strategy that has been shown to be useful to favor exploration in learning algorithms: the application of noise during learning. Finally, to check if the increased exploration hypothesis holds, we visualize the genotypic space during learning considering the different optimization strategies by using the Search Trajectory Network representation. The results indicate that noise and growth increase exploration, but only growth guides the search towards good solutions.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83851760","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

Search-Based Test Generation Targeting Non-Functional Quality Attributes of Android Apps 针对Android应用非功能质量属性的基于搜索的测试生成

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590449

Teklit Gereziher, Selam Gebrekrstos, Gregory Gay

引用次数: 0

RM-SAEA: Regularity Model Based Surrogate-Assisted Evolutionary Algorithms for Expensive Multi-Objective Optimization 基于规则模型的代理辅助进化算法的昂贵多目标优化

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590435

Yongfan Lu, Bingdong Li, Hong Qian, W. Hong, Peng Yang, Aimin Zhou

{"title":"RM-SAEA: Regularity Model Based Surrogate-Assisted Evolutionary Algorithms for Expensive Multi-Objective Optimization","authors":"Yongfan Lu, Bingdong Li, Hong Qian, W. Hong, Peng Yang, Aimin Zhou","doi":"10.1145/3583131.3590435","DOIUrl":"https://doi.org/10.1145/3583131.3590435","url":null,"abstract":"Due to computationally and/or financially costly evaluation, tackling expensive multi-objective optimization problems is quite challenging for evolutionary algorithms. One popular approach to these problems is building cheap surrogate models to replace the expensive real function evaluations. To this end, various kinds of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed, building surrogate models which predict the fitness values, classifications, or relation of the candidate solutions. However, off-spring generation, despite its important role in evolutionary optimization, has not received enough attention in these SAEAs. In this paper, a regularity model based framework, namely RM-SAEA, is proposed for better offspring generation in expensive multi-objective optimization. To be specific, RM-SAEA is featured with a heterogeneous offspring generation module, which is composed of a regularity model and a general genetic operator. Moreover, in order to alleviate the data deficiency issue in the expensive optimization scenario, a data augmentation strategy is employed while training the regularity model. Finally, two representative SAEAs are embedded into RM-SAEA in order to instantiate the proposed framework. Experimental results on benchmark multi-objective problems with up to 10 objectives demonstrate that RM-SAEA achieves the best overall performance compared with 6 state-of-the-art algorithms.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72885320","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

Learning Emergency Medical Dispatch Policies Via Genetic Programming 通过遗传规划学习紧急医疗调度政策

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590434

Jordan MacLachlan, Yi Mei, Fangfang Zhang, Mengjie Zhang, Jessica Signal

{"title":"Learning Emergency Medical Dispatch Policies Via Genetic Programming","authors":"Jordan MacLachlan, Yi Mei, Fangfang Zhang, Mengjie Zhang, Jessica Signal","doi":"10.1145/3583131.3590434","DOIUrl":"https://doi.org/10.1145/3583131.3590434","url":null,"abstract":"Of great value to modern municipalities is the task of emergency medical response in the community. Resource allocation is vital to ensure minimal response times, which we may perform via human experts or automate by maximising ambulance coverage. To combat black-box modelling, we propose a modularised Genetic Programming Hyper Heuristic framework to learn the five key decisions of Emergency Medical Dispatch (EMD) within a reactive decision-making process. We minimise the representational distance between our work and reality by working with our local ambulance service to design a set of heuristics approximating their current decision-making processes and a set of synthetic datasets influenced by existing patterns in practice. Through our modularised framework, we learn each decision independently to identify those most valuable to EMD and learn all five decisions simultaneously, improving performance by 69% on the largest novel dataset. We analyse the decision-making logic behind several learned rules to further improve our understanding of EMD. For example, we find that emergency urgency is not necessarily considered when dispatching idle ambulances in favour of maximising fleet availability.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75617974","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

Adaptive Search-based Repair of Deep Neural Networks 基于自适应搜索的深度神经网络修复

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590477

Davide Li Calsi, Matias Duran, Thomas Laurent, Xiaoyi Zhang, Paolo Arcaini, F. Ishikawa

{"title":"Adaptive Search-based Repair of Deep Neural Networks","authors":"Davide Li Calsi, Matias Duran, Thomas Laurent, Xiaoyi Zhang, Paolo Arcaini, F. Ishikawa","doi":"10.1145/3583131.3590477","DOIUrl":"https://doi.org/10.1145/3583131.3590477","url":null,"abstract":"Deep Neural Networks (DNNs) are finding a place at the heart of more and more critical systems, and it is necessary to ensure they perform in as correct a way as possible. Search-based repair methods, that search for new values for target neuron weights in the network to better process fault-inducing inputs, have shown promising results. These methods rely on fault localisation to determine what weights the search should target. However, as the search progresses and the network evolves, the weights responsible for the faults in the system will change, and the search will lose in effectiveness. In this work, we propose an adaptive search method for DNN repair that adaptively updates the target weights during the search by performing fault localisation on the current state of the model. We propose and implement two methods to decide when to update the target weights, based on the progress of the search's fitness value or on the evolution of fault localisation results. We apply our technique to two image classification DNN architectures against a dataset of autonomous driving images, and compare it with a state-of-the art search-based DNN repair approach.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"244 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80562527","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