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MPCS: Neuroplastic Continual Learning via Multi-Component Plasticity and Topology-Aware EWC Combining Trained Models in Reinforcement Learning Training Non-Differentiable Networks via Optimal Transport ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance Benchmarking local Hebbian learning rules for memory storage and prototype extraction Robust volatility updates for Hierarchical Gaussian Filtering Spiking Sequence Machines and Transformers Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks Attractor FCM Physical Foundation Models: Fixed hardware implementations of large-scale neural networks When Does Structure Matter in Continual Learning? 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Predicting Performance from Separability and Similarity Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks Why Fine-Tuning Encourages Hallucinations and How to Fix It Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning Structure as Computation: Developmental Generation of Minimal Neural Circuits NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning Neural architectures for resolving references in program code Diffusion Language Models for Speech Recognition A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot On the Use of Evolutionary Optimization for the Dynamic Chance Constrained Open-Pit Mine Scheduling Problem Analog Optical Inference on Million-Record Mortgage Data Shapley Value-Guided Adaptive Ensemble Learning for Explainable Financial Fraud Detection with U.S. Regulatory Compliance Validation Does Dimensionality Reduction via Random Projections Preserve Landscape Features? Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems Beyond LLMs, Sparse Distributed Memory, and Neuromorphics <A Hyper-Dimensional SRAM-CAM "VaCoAl" for Ultra-High Speed, Ultra-Low Power, and Low Cost> SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression Evolving Many Worlds: Towards Open-Ended Discovery in Petri Dish NCA via Population-Based Training Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds Retinal Cyst Detection from Optical Coherence Tomography Images TurboEvolve: Towards Fast and Robust LLM-Driven Program Evolution Universal statistical signatures of evolution in artificial intelligence architectures Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks Sequential KV Cache Compression via Probabilistic Language Tries: Beyond the Per-Vector Shannon Limit Evolutionary Token-Level Prompt Optimization for Diffusion Models Hierarchical Kernel Transformer: Multi-Scale Attention with an Information-Theoretic Approximation Analysis A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need Multi-Modal Learning meets Genetic Programming: Analyzing Alignment in Latent Space Optimization OpenCLAW-P2P v7.0-P2PCLAW: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review v7.0 -- Mathematical Corrections & Ecosystem Developments Edition An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing Selectivity and Shape in the Design of Forward-Forward Goodness Functions Efficient Disruption of Criminal Networks through Multi-Objective Genetic Algorithms DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis EvoForest: A Novel Machine-Learning Paradigm via Open-Ended Evolution of Computational Graphs Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation Rethinking LLM-Driven Heuristic Design: Generating Efficient and Specialized Solvers via Dynamics-Aware Optimization Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces QSLM: A Performance- and Memory-aware Quantization Framework with Tiered Search Strategy for Spike-driven Language Models Optimized Architectures for Kolmogorov-Arnold Networks AP-BMM: Approximating Capability-Cost Pareto Sets of LLMs via Asynchronous Prior-Guided Bayesian Model Merging Transformer Semantic Genetic Programming for d-dimensional Symbolic Regression Problems Efficient Vector Symbolic Architectures from Histogram Recovery Language Models Learn Universal Representations of Numbers and Here's Why You Should Care A Practitioner's Guide to Kolmogorov-Arnold Networks Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems HiPreNets: High-Precision Neural Networks through Progressive Training Machine Learning as Iterated Belief Change a la Darwiche and Pearl Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents Learning Evolution via Optimization Knowledge Adaptation Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers P1-KAN: an effective Kolmogorov-Arnold network with application to hydraulic valley optimization
New Model for Multi-Objective Evolutionary Algorithms
Bojin Zheng, Yuanxiang Li · 2011-01-31 · via cs.NE updates on arXiv.org

Multi-Objective Evolutionary Algorithms (MOEAs) have been proved efficient to deal with Multi-objective Optimization Problems (MOPs). Until now tens of MOEAs have been proposed. The unified mode would provide a more systematic approach to build new MOEAs. Here a new model is proposed which includes two sub-models based on two classes of different schemas of MOEAs. According to the new model, some representatives algorithms are decomposed and some interesting issues are discussed.

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