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

Cost-Aware Active Learning for Feasible Region Identification 可行区域识别的成本意识主动学习

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596323

I. Nikova, T. Dhaene, I. Couckuyt

引用次数: 0

Bayesian Optimization For Choice Data 选择数据的贝叶斯优化

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596324

A. Benavoli, Dario Azzimonti, D. Piga

引用次数: 2

Personalized Group Itinerary Recommendation using a Knowledge-based Evolutionary Approach 基于知识进化方法的个性化团体行程推荐

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596345

Farzaneh Jouyandeh, Pooya Moradian Zadeh

{"title":"Personalized Group Itinerary Recommendation using a Knowledge-based Evolutionary Approach","authors":"Farzaneh Jouyandeh, Pooya Moradian Zadeh","doi":"10.1145/3583133.3596345","DOIUrl":"https://doi.org/10.1145/3583133.3596345","url":null,"abstract":"The problem of recommending a group itinerary is considered to be NP-hard and can be defined as an optimization problem. The goal is to recommend the best series of points of interest (POIs) to a group of people who are visiting a destination based on their preferences and past experiences. This paper proposes an evolutionary approach based on cultural algorithms to address this problem. Our objective is to maximize the group's satisfaction by recommending an itinerary comprised of the optimal series of visiting POIs, considering the interests of all members, total travel time, and visit duration while minimizing the travel costs within their assigned budget. The proposed algorithm uses historical and normative knowledge to create a belief space used later to guide the search direction and decision-making. The belief space is a knowledge repository that tracks the evolution of decisions during the search process. We evaluated the performance of the proposed algorithm on a set of real-world datasets and compared that with state-of-the-art approaches. We also conducted non-parametric tests to analyze the results. Compared with other algorithms, the proposed approach is capable of recommending efficient and satisfactory itineraries to groups with diverse interests.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115758886","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

CRACS: Compaction of Rules in Anticipatory Classifier Systems 预期分类器系统中规则的压缩

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596352

Romain Orhand, P. Collet, P. Parrend, A. Jeannin-Girardon

{"title":"CRACS: Compaction of Rules in Anticipatory Classifier Systems","authors":"Romain Orhand, P. Collet, P. Parrend, A. Jeannin-Girardon","doi":"10.1145/3583133.3596352","DOIUrl":"https://doi.org/10.1145/3583133.3596352","url":null,"abstract":"Rule Compaction of populations of Learning Classifier Systems (LCS) has always been a topic of interest to get more insights into the discovered underlying patterns from the data or to remove useless classifiers from the populations. However, these techniques have neither been used nor adapted to Anticipatory Learning Classifier Systems (ALCS). ALCS differ from other LCS in that they build models of their environments from which decision policies to solve their learning tasks are learned. We thus propose CRACS (Compaction of Rules in Anticipatory Classifier Systems), a compaction algorithm for ALCS that aims to reduce the size of their environmental models without impairing these models or the ability of these systems to solve their tasks. CRACS relies on filters applied to classifiers and subsumption principles. The capabilities of our compaction algorithm have been studied with three different ALCS on a thorough benchmark of 23 mazes of various levels of environmental uncertainty. The results show that CRACS reduces the size of populations of classifiers while the learned models of environments and the ability of ALCS to solve their tasks are preserved.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123159296","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

Incremental Recursive Ranking Grouping -- A Decomposition Strategy for Additively and Nonadditively Separable Problems 增量递归排序分组——一种可加和不可加可分问题的分解策略

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3595846

M. Komarnicki, M. Przewozniczek, H. Kwasnicka, K. Walkowiak

{"title":"Incremental Recursive Ranking Grouping -- A Decomposition Strategy for Additively and Nonadditively Separable Problems","authors":"M. Komarnicki, M. Przewozniczek, H. Kwasnicka, K. Walkowiak","doi":"10.1145/3583133.3595846","DOIUrl":"https://doi.org/10.1145/3583133.3595846","url":null,"abstract":"Many real-world optimization problems may be classified as Large-Scale Global Optimization (LSGO) problems. When these high-dimensional problems are continuous, it was shown effective to embed a decomposition strategy into a Cooperative Co-Evolution (CC) framework. The effectiveness of the method that decomposes a problem into subproblems and optimizes them separately may depend on the decomposition accuracy and cost. Recent decomposition strategy advances focus mainly on Differential Grouping (DG). However, when a considered problem is nonadditively separable, DG-based strategies may report some variables as interacting, although the interaction between them does not exist. Monotonicity checking strategies do not suffer from this disadvantage. However, they suffer from another decomposition inaccuracy - monotonicity checking strategies may miss discovering many existing interactions. Therefore, Incremental Recursive Ranking Grouping (IRRG) is a new proposition that accurately decomposes both additively and nonadditively separable problems. The decomposition cost of IRRG is higher when compared with Recursive DG 3 (RDG3). Since the higher cost was a negligible part of the overall computational budget, optimization results of the considered CC frameworks were affected mainly by the decomposition accuracy.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123186655","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 high-dimensional feature set of hyperspectral measurements for plant phenotype classification 减少植物表型分类高光谱测量的高维特征集

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596941

B. Ruszczak

引用次数: 0

Generative Meta-Learning Robust Quality-Diversity Portfolio 生成式元学习稳健质量-多样性组合

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590729

K. Yuksel

{"title":"Generative Meta-Learning Robust Quality-Diversity Portfolio","authors":"K. Yuksel","doi":"10.1145/3583133.3590729","DOIUrl":"https://doi.org/10.1145/3583133.3590729","url":null,"abstract":"This paper proposes a novel meta-learning approach to optimize a robust portfolio ensemble. The method uses a deep generative model to generate diverse and high-quality sub-portfolios combined to form the ensemble portfolio. The generative model consists of a convolutional layer, a stateful LSTM module, and a dense network. During training, the model takes a randomly sampled batch of Gaussian noise and outputs a population of solutions, which are then evaluated using the objective function of the problem. The weights of the model are updated using a gradient-based optimizer. The convolutional layer transforms the noise into a desired distribution in latent space, while the LSTM module adds dependence between generations. The dense network decodes the population of solutions. The proposed method balances maximizing the performance of the sub-portfolios with minimizing their maximum correlation, resulting in a robust ensemble portfolio against systematic shocks. The approach was effective in experiments where stochastic rewards were present. Moreover, the results (Fig. 1) demonstrated that the ensemble portfolio obtained by taking the average of the generated sub-portfolio weights was robust and generalized well. The proposed method can be applied to problems where diversity is desired among co-optimized solutions for a robust ensemble. The source-codes and the dataset are in the supplementary material.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121765171","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 Solution Combination Techniques in Scatter Search for Quadratic Unconstrained Binary Optimization 二次型无约束二元优化散点搜索中解组合技术的比较

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596319

Justin Pauckert, Pieter Debevere, Matthieu Parizy, M. Ayodele

{"title":"Comparing Solution Combination Techniques in Scatter Search for Quadratic Unconstrained Binary Optimization","authors":"Justin Pauckert, Pieter Debevere, Matthieu Parizy, M. Ayodele","doi":"10.1145/3583133.3596319","DOIUrl":"https://doi.org/10.1145/3583133.3596319","url":null,"abstract":"Quadratic Unconstrained Binary Optimization (QUBO) has emerged as a vital unifying model for combinatorial optimization problems, and (meta-)heuristic approaches are commonly used to solve them due to their NP-hard nature. Scatter Search (SS), a population-based metaheuristic framework, is one such method that has shown promising results for QUBO problems. Generating new solutions from more promising ones is a crucial operation in SS. Path Relinking (PR) based SS has been previously used to solve challenging QUBO problems with high-quality solutions. This paper introduces two new variants of the SS algorithm. The first is the (Multi) Uniform Crossover (MUC) based SS while the second is the Univariate Marginal Distribution Algorithm (UMDA) based SS. MUC and UMDA are well-known operators in Genetic Algorithms and Estimation of Distribution Algorithms respectively. When compared to the existing PR based SS, this work shows that more promising results can be achieved when the newly proposed MUC and UMDA-based SS are applied to QUBO formulations of the Quadratic Knapsack Problem (QKP) instances.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122025472","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

Overcoming Deceptive Rewards with Quality-Diversity 用质量多样性克服欺骗性奖励

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590741

A. Feiden, J. Garcke

引用次数: 0

Fast generation of centroids for MAP-Elites 快速生成map - elite的质心

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590726

Jean-Baptiste Mouret

引用次数: 0