{"product_id":"9783032156341","title":"Computational Intelligence : 17th International Joint Conference, IJCCI 2025, Marbella, Spain, October 22-24, 2025, Proceedings, Part II (Communications in Computer and Information Science)","description":"\u003cp\u003eThis three-volume set CCIS 2827-2829 constitutes the refereed proceedings of the 17th International Joint Conference on Computational Intelligence, IJCCI 2025, held in Marbella, Spain, during October 22 24, 2025.\u003c\/p\u003e\u003cp\u003eThe 36 full papers and 83 short papers included in these volumes were carefully reviewed and selected from 146 submissions. They are organized into the following topical sections: \n\u003c\/p\u003e\u003cp\u003ePart I: International Conference on Agentic and Generative Techniques in Intelligent Computational Systems; International Conference on Fuzzy Computation Theory and Applications.\nPart II: International Conference on Evolutionary Computation Theory and Applications.\nPart III: International Conference on Explainable AI for Neural and Symbolic Methods; International Conference on Neural Computation Theory and Applications.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e.- International Conference on Evolutionary Computation Theory and Applications.\u003c\/strong\u003e\n.- Evolutionary Multi-Objective Optimization of Unmanned Aerial Base Station Placement for Power Grid Recovery.\n.- A Constraint-Handling Method for Model-Building Genetic Algorithm: Three-Population Scheme.\n.-  Genetic Programming with Ranging-Binding Mechanism for Symbolic Regression.\n.- Comparative Analysis of Performance Predictors in Multi- Objective Neural Architecture Search for Image Super-Resolution: XGBoost Regressor and SynFlow.\n.- Scaling Up Pareto Local Optimal Solutions Networks: Modelling Multi-Objective Landscapes.\n.- Comparative Analysis of Model Selection Criteria for Symbolic Regression Using Genetic Programming.\n.- Automatic Synthesis of Selection Operators in Genetic Algorithms Using FunSearch.\n.- Evaluating Fitness Averaging Strategies in Cooperative NeuroCoEvolution for Automated Soft Actuator Design.\n.- Hierarchical Self-Evaluated Topology PSO with Grouping and Mediator.\n.- GADA: An Adaptive Genetic Algorithm-Based Framework for Dynamic University Course Timetabling.\n.- Edge Weight Learning Approaches for the Promotion of Fairness in the Dictator Game. \n.- An Adaptive Redundancy-Aware Binary Grey Wolf Optimizer for\n Feature Selection.\n.- Enhanced Adaptive Differential Evolution for Optimized Ensemble Learning in Diabetic Retinopathy Diagnosis.\n.- SCOPE for Hexapod Gait Generation.\n.- Evolving Neural Controllers for Xpilot-AI Racing Using Neuroevolution of Augmenting Topologies.\n.- Niching Agents in The Core.\n.- Decentralized Evolution of Hexapod Gaits with Independent Leg\n Controllers.\n.-  Novel Discretization Scheme for Multidimensional Split-on-Demand on Real-Valued Optimization with High Multi-Modality.\n.- Investigation of Behavioral Cloning Guided Genetic Programming Using a Multilayer Perceptron for Symbolic Regression.\n.- Extending Cartesian Genetic Programming via Iterative Subgraph Assessment.\n.- Robustness of Evolved Strategies: An Application to Cycling.\n.- Incremental Evolution of Fault-Tolerant Gaits in Octopod Robots.\n.- Behaviour Space Analysis of LLM-Driven Meta-Heuristic Discovery.\n.- Sustainable Performance Improvement of Surrogate-Assisted Evolutionary Algorithms Using Tabu Search.\n.- A Comparative Study of Binary-Coded Red Deer and Genetic Algorithms\n.- An Overview of Genetic Algorithms in Educational Research from 2020 to 2024.\n.- Evolution of Wavelets and Hardware for Satellite Image Compression.\n.-  Using an Integer Condensed Population for Resource-Constrained Evolution. \n.-  Integral Maude Operation Semantics for Algebraic Petri Nets: An Effective Model of Adaptive Systems.\n.- The Traveling Tournament Problem: Valid Solutions are Different Across Instance Sizes.\n.- A Surrogate-Assisted Co-Evolutionary Framework for Bilevel Optimization.\n.-  SA-GA: Surrogate-Assisted Genetic Algorithm for Optimizing Activation Function Approximations in Privacy-Preserving EEG Classification Networks.\n.-  Solving the Electric Vehicle Routing Problem with Nonlinear Charging Functions Using Genetic Programming.\n.- Optimization Is not Enough: Why Problem Formulation Deserves Equal Attention.\u003c\/p\u003e","brand":"Springer, Berlin","offers":[{"title":"Default Title","offer_id":45670998081615,"sku":"00000_00000_00000_00000","price":3500.0,"currency_code":"TWD","in_stock":true}],"url":"https:\/\/kinokuniya.com.tw\/products\/9783032156341","provider":"Books Kinokuniya Taiwan","version":"1.0","type":"link"}