Proceedings of the Companion Conference on Genetic and Evolutionary Computation最新文献_第3页

Fast Genetic Algorithm for feature selection — A qualitative approximation approach 特征选择的快速遗传算法-一种定性近似方法

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3595823

Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, P. Mashhadi

{"title":"Fast Genetic Algorithm for feature selection — A qualitative approximation approach","authors":"Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, P. Mashhadi","doi":"10.1145/3583133.3595823","DOIUrl":"https://doi.org/10.1145/3583133.3595823","url":null,"abstract":"We propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. The proposed approach involves constructing a lightweight qualitative meta-model by sub-sampling data instances and then using this meta-model to carry out the feature selection task. We define \"Approximation Usefulness\" to capture the necessary conditions that allow the meta-model to lead the evolutionary computations to the correct maximum of the fitness function. Based on our procedure we create CHCQX a Qualitative approXimations variant of the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation). We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy, particularly for large datasets with over 100K instances. We also demonstrate the applicability of our approach to Swarm Intelligence (SI), with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available2. This paper for the Hot-off-the-Press track at GECCO 2023 summarizes the original work published at [3].","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"13 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":"127778912","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: 9

An Efficient QAOA via a Polynomial QPU-Needless Approach 基于多项式无需qpu方法的高效QAOA

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596409

F. Chicano, Z. Dahi, Gabriel Luque

{"title":"An Efficient QAOA via a Polynomial QPU-Needless Approach","authors":"F. Chicano, Z. Dahi, Gabriel Luque","doi":"10.1145/3583133.3596409","DOIUrl":"https://doi.org/10.1145/3583133.3596409","url":null,"abstract":"The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum algorithm described as ansatzes that represent both the problem and the mixer Hamiltonians. Both are parameterizable unitary transformations executed on a quantum machine/simulator and whose parameters are iteratively optimized using a classical device to optimize the problem's expectation value. To do so, in each QAOA iteration, most of the literature uses a quantum machine/simulator to measure the QAOA outcomes. However, this poses a severe bottleneck considering that quantum machines are hardly constrained (e.g. long queuing, limited qubits, etc.), likewise, quantum simulation also induces exponentially-increasing memory usage when dealing with large problems requiring more qubits. These limitations make today's QAOA implementation impractical since it is hard to obtain good solutions with a reasonably-acceptable time/resources. Considering these facts, this work presents a new approach with two main contributions, including (I) removing the need for accessing quantum devices or large-sized classical machines during the QAOA optimization phase, and (II) ensuring that when dealing with some k-bounded pseudo-Boolean problems, optimizing the exact problem's expectation value can be done in polynomial time using a classical computer.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"28 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":"126430211","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

Human-Driven Genetic Programming for Program Synthesis: A Prototype 人类驱动的程序综合遗传规划:一个原型

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596373

Thomas Helmuth, James Gunder Frazier, Yu-me Shi, A. Abdelrehim

引用次数: 0

Quality Diversity optimization using the IsoLineDD operator: forward and backward directions are equally important 使用IsoLineDD算子的质量分集优化:向前和向后方向同样重要

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590737

Konstantinos Christou, C. Christodoulou, Vassilis Vassiliades

{"title":"Quality Diversity optimization using the IsoLineDD operator: forward and backward directions are equally important","authors":"Konstantinos Christou, C. Christodoulou, Vassilis Vassiliades","doi":"10.1145/3583133.3590737","DOIUrl":"https://doi.org/10.1145/3583133.3590737","url":null,"abstract":"Quality Diversity (QD) optimization aims at returning a diverse collection of high quality solutions in a single run. Prior work indicated that the optimized collection concentrates in a subspace of the genotype space called the Elite Hypervolume. This suggested that if the Elite Hypervolume is convex, perturbing a solution (elite) towards the direction of another elite would create a solution inside the subspace. To accelerate QD optimization, the IsoLineDD operator was proposed which perturbs a solution along the line that connects it with another elite, both in the forward and the backward direction (i.e., towards and away from another elite), with equal probability. In this work, we hypothesize that the backward direction is mostly useful for exploration, at the beginning of QD optimization, rather than exploitation. To validate our hypothesis, we create a dynamic IsoLineDD operator that modifies its probability of creating solutions in the forward and backward directions, respectively. Our experiments and analysis in the robotic arm repertoire and Rastrigin problems invalidate this hypothesis by demonstrating that the forward and backward directions of vanilla IsoLineDD equally contribute to the optimization of the collection.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"53 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":"134206889","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

Benchmarking CMA-ES with Basic Integer Handling on a Mixed-Integer Test Problem Suite 基于基本整数处理的CMA-ES混合整数测试问题集的基准测试

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596411

Tristan Marty, Y. Semet, A. Auger, Sébastien Héron, N. Hansen

引用次数: 0

Performance Analysis of Self-Supervised Strategies for Standard Genetic Programming 标准遗传规划的自监督策略性能分析

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590748

Nuno M. Rodrigues, J. Almeida, Sara Silva

引用次数: 0

Parametrizing GP Trees for Better Symbolic Regression Performance through Gradient Descent. 通过梯度下降参数化GP树以获得更好的符号回归性能。

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590574

Gloria Pietropolli, Federico Julian Camerota Verdù, L. Manzoni, M. Castelli

引用次数: 0

Exploratory Landscape Analysis Based Parameter Control 基于参数控制的探索性景观分析

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596364

M. Pikalov, Aleksei Pismerov

引用次数: 0

A New Metaheuristic-Algorithm Similarity Measure Using Signal Flow Diagrams 一种新的基于信号流图的元启发式算法相似度度量

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590692

T. Achary, A. Pillay, E. Jembere

引用次数: 0

Maximizing Efficiency: A Comparative Study of SOMA Algorithm Variants and Constraint Handling Methods for Time Delay System Optimization 效率最大化:时滞系统优化的SOMA算法变体与约束处理方法的比较研究

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596417

R. Šenkeřík, T. Kadavy, Peter Janků, Michal Pluhacek, Hubert Guzowski, L. Pekař, R. Matušů, Adam Viktorin, M. Smółka, A. Byrski, Zuzana Kominkova Oplatkova

{"title":"Maximizing Efficiency: A Comparative Study of SOMA Algorithm Variants and Constraint Handling Methods for Time Delay System Optimization","authors":"R. Šenkeřík, T. Kadavy, Peter Janků, Michal Pluhacek, Hubert Guzowski, L. Pekař, R. Matušů, Adam Viktorin, M. Smółka, A. Byrski, Zuzana Kominkova Oplatkova","doi":"10.1145/3583133.3596417","DOIUrl":"https://doi.org/10.1145/3583133.3596417","url":null,"abstract":"This paper presents an experimental study that compares four adaptive variants of the self-organizing migrating algorithm (SOMA). Each variant uses three different constraint handling methods for the optimization of a time delay system model. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delayed systems to develop more effective and efficient control strategies and precise model identifications. The study includes a detailed description of the selected variants of the SOMA and the adaptive mechanisms used. A complex workflow of experiments is described, and the results and discussion are presented. The experimental results highlight the effectiveness of the SOMA variants with specific constraint handling methods for time delay system optimization. Overall, this study contributes to the understanding of the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of the SOMA variants and can help guide the selection of appropriate constraint handling methods and the adaptive mechanisms of metaheuristics.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"26 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":"127631534","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