Genetic Programming and Evolvable Machines最新文献_第3页

Jaws 30 下巴30

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-11-22 DOI: 10.1007/s10710-023-09467-x

W. B. Langdon

引用次数: 0

Response to comments on “Jaws 30” 对《大白鲨30》评论的回应

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-11-22 DOI: 10.1007/s10710-023-09474-y

W. B. Langdon

引用次数: 0

Is the evolution metaphor still necessary or even useful for genetic programming? 进化隐喻对于基因编程是否仍有必要甚至有用？

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-11-22 DOI: 10.1007/s10710-023-09469-9

Jason H. Moore

引用次数: 0

New directions in fitness evaluation: commentary on Langdon’s JAWS30 体能评估的新方向：对 Langdon's JAWS 的评论30

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-11-22 DOI: 10.1007/s10710-023-09470-2

Colin G. Johnson

引用次数: 0

W. B. Langdon “Jaws 30” w·b·兰登《大白鲨30》

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-11-22 DOI: 10.1007/s10710-023-09473-z

Malcolm I. Heywood

引用次数: 0

Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search 去噪自编码器遗传规划:搜索中控制探索和开发的策略

3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-11-08 DOI: 10.1007/s10710-023-09462-2

David Wittenberg, Franz Rothlauf, Christian Gagné

{"title":"Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search","authors":"David Wittenberg, Franz Rothlauf, Christian Gagné","doi":"10.1007/s10710-023-09462-2","DOIUrl":"https://doi.org/10.1007/s10710-023-09462-2","url":null,"abstract":"Abstract Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming approach that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming. At each generation, the idea is to capture promising properties of the parent population in a probabilistic model and to use corruption to transfer variations of these properties to the offspring. This work studies the influence of corruption and sampling steps on search. Corruption partially mutates candidate solutions that are used as input to the model, whereas the number of sampling steps defines how often we re-use the output during model sampling as input to the model. We study the generalization of the royal tree problem, the Airfoil problem, and the Pagie-1 problem, and find that both corruption strength and the number of sampling steps influence exploration and exploitation in search and affect performance: exploration increases with stronger corruption and lower number of sampling steps. The results indicate that both corruption and sampling steps are key to the success of the DAE-GP: it permits us to balance the exploration and exploitation behavior in search, resulting in an improved search quality. However, also selection is important for exploration and exploitation and should be chosen wisely.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":" 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135340710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

On the hybridization of geometric semantic GP with gradient-based optimizers 几何语义GP与梯度优化器的杂交研究

3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-10-28 DOI: 10.1007/s10710-023-09463-1

Gloria Pietropolli, Luca Manzoni, Alessia Paoletti, Mauro Castelli

引用次数: 0

Semantic segmentation network stacking with genetic programming 基于遗传规划的语义分割网络叠加

3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-10-26 DOI: 10.1007/s10710-023-09464-0

Illya Bakurov, Marco Buzzelli, Raimondo Schettini, Mauro Castelli, Leonardo Vanneschi

{"title":"Semantic segmentation network stacking with genetic programming","authors":"Illya Bakurov, Marco Buzzelli, Raimondo Schettini, Mauro Castelli, Leonardo Vanneschi","doi":"10.1007/s10710-023-09464-0","DOIUrl":"https://doi.org/10.1007/s10710-023-09464-0","url":null,"abstract":"Abstract Semantic segmentation consists of classifying each pixel of an image and constitutes an essential step towards scene recognition and understanding. Deep convolutional encoder–decoder neural networks now constitute state-of-the-art methods in the field of semantic segmentation. The problem of street scenes’ segmentation for automotive applications constitutes an important application field of such networks and introduces a set of imperative exigencies. Since the models need to be executed on self-driving vehicles to make fast decisions in response to a constantly changing environment, they are not only expected to operate reliably but also to process the input images rapidly. In this paper, we explore genetic programming (GP) as a meta-model that combines four different efficiency-oriented networks for the analysis of urban scenes. Notably, we present and examine two approaches. In the first approach, we represent solutions as GP trees that combine networks’ outputs such that each output class’s prediction is obtained through the same meta-model. In the second approach, we propose representing solutions as lists of GP trees, each designed to provide a unique meta-model for a given target class. The main objective is to develop efficient and accurate combination models that could be easily interpreted, therefore allowing gathering some hints on how to improve the existing networks. The experiments performed on the Cityscapes dataset of urban scene images with semantic pixel-wise annotations confirm the effectiveness of the proposed approach. Specifically, our best-performing models improve systems’ generalization ability by approximately 5% compared to traditional ensembles, 30% for the less performing state-of-the-art CNN and show competitive results with respect to state-of-the-art ensembles. Additionally, they are small in size, allow interpretability, and use fewer features due to GP’s automatic feature selection.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"32 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A probabilistic meta-heuristic optimisation algorithm for image multi-level thresholding 一种用于图像多级阈值分割的概率启发式优化算法

3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-10-24 DOI: 10.1007/s10710-023-09460-4

Mohammad Hassan Tayarani Najaran

引用次数: 0

Alleviating overfitting in transformation-interaction-rational symbolic regression with multi-objective optimization 缓解多目标优化转化-交互-理性符号回归中的过拟合

3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2023-10-20 DOI: 10.1007/s10710-023-09461-3

Fabrício Olivetti de França

引用次数: 0