05728nam a22005895i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001700172072001600189072002300205072001500228245027000243250001800513264006900531300006700600336002600667337002600693338003600719347002400755490005900779505227000838520106403108650002904172650002204201650003204223650003204255650002204287650004204309650003404351650003004385650004404415650005104459650003604510650003704546700008404583700008504667700008904752710003404841773002604875776003604901776003604937830005904973856004605032912001405078912001505092912001405107999001705121978-3-031-56855-8DE-He21320251030111741.0cr nn 008mamaa240323s2024    sz |    s    |||| 0|eng d  a97830315685589978-3-031-56855-87 a10.1007/978-3-031-56855-82doi 4aQA75.5-76.95 7aUYA2bicssc 7aCOM0140002bisacsh 7aUYA2thema10aApplications of Evolutionary Computationh[electronic resource] :b27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part II /cedited by Stephen Smith, João Correia, Christian Cintrano.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXXI, 409 p. 151 illus., 125 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aLecture Notes in Computer Science,x1611-3349 ;v146350 aEvolutionary Machine Learning: Hindsight Experience Replay with Evolutionary Decision Trees for Curriculum Goal Generation -- Cultivating Diversity: A Comparison of Diversity Objectives in Neuroevolution -- Evolving Reservoirs for Meta Reinforcement Learning -- Hybrid Surrogate Assisted Evolutionary Multiobjective Reinforcement Learning for Continuous Robot Control -- Towards Physical Plausibility in Neuroevolution Systems -- Leveraging More of Biology in Evolutionary Reinforcement Learning -- A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search Behaviour -- DeepEMO: A Multi-Indicator Convolutional Neural Network-based Evolutionary Multi-Objective Algorithm -- A Comparative Analysis of Evolutionary Adversarial One-Pixel Attacks -- Robust Neural Architecture Search using Differential Evolution for Medical Images -- Progressive Self-Supervised Multi-Objective NAS for Image Classification -- Genetic Programming with Aggregate Channel Features for Flower Localization Using Limited Training Data -- Evolutionary Multi-Objective Optimization of Large Language Model Prompts for Balancing Sentiments -- Evolutionary Feature-Binning with Adaptive Burden Thresholding for Biomedical Risk Stratification -- An Evolutionary Deep Learning Approach for Efficient Quantum Algorithms Transpilation -- Measuring Similarities in Model Structure of Metaheuristic Rule Set Learners -- Machine Learning and AI in Digital Healthcare and Personalized Medicine: Incremental growth on Compositional Pattern Producing Networks based optimization of biohybrid actuators -- Problem Landscape Analysis for Efficient Optimization: Hilbert Curves for Efficient Exploratory Landscape Analysis Neighbourhood Sampling -- Predicting Algorithm Performance in Constrained Multiobjective Optimization: A Tough Nut to Crack -- On the Latent Structure of the bbob-biobj Test Suite -- Soft Computing applied to Games -- Strategies for Evolving Diverse and Effective Behaviours in Pursuit Domains -- Using Evolution and Deep Learning to Generate Diverse Intelligent Agents -- Vision Transformers for Computer Go -- Surrogate-Assisted Evolutionary Optimisation: Integrating Bayesian and Evolutionary Approaches for Multi-Objective Optimisation.  aThe two-volume set LNCS 14634 and 14635 constitutes the refereed proceedings of the 27th European Conference on Applications of Evolutionary Computation, EvoApplications 2024, held as part of EvoStar 2024, in Aberystwyth, UK, April 3–5, 2024, and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EuroGP. The 51 full papers presented in these proceedings were carefully reviewed and selected from 77 submissions. The papers have been organized in the following topical sections: applications of evolutionary computation; analysis of evolutionary computation methods: theory, empirics, and real-world applications; computational intelligence for sustainability; evolutionary computation in edge, fog, and cloud computing; evolutionary computation in image analysis, signal processing and pattern recognition; evolutionary machine learning; machine learning and AI in digital healthcare and personalized medicine; problem landscape analysis for efficient optimization; softcomputing applied to games; and surrogate-assisted evolutionary optimisation. 0aComputer science.958552 0aComputers.965793 0aComputer networks .960157 0aComputers, Special purpose. 0aComputer systems. 0aComputer sciencexMathematics.96404114aTheory of Computation.96755224aComputing Milieux.96653024aComputer Communication Networks.96568924aSpecial Purpose and Application-Based Systems.24aComputer System Implementation.24aMathematics of Computing.9665011 aSmith, Stephen.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9708021 aCorreia, João.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9735261 aCintrano, Christian.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9735272 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303156854108iPrinted edition:z9783031568565 0aLecture Notes in Computer Science,x1611-3349 ;v1463540uhttps://doi.org/10.1007/978-3-031-56855-8  aZDB-2-SCS  aZDB-2-SXCS  aZDB-2-LNC  c58610d58610