Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm design, and adaptive decision-making. This survey reviews the bidirectional interaction between these two paradigms and examines how their complementary strengths can be leveraged in hybrid intelligent systems. First, we analyze how EC can enhance LLM-based systems through prompt optimization, hyperparameter tuning, and architecture search. Second, we review how LLMs can improve EC by supporting metaheuristic design, surrogate reasoning, adaptive operator control, and heuristic generation. We further discuss emerging co-adaptive frameworks in which LLMs and EC interact through iterative feedback loops. Beyond summarizing recent developments, the survey provides a structured perspective on interaction mechanisms, application patterns, and methodological challenges, including computational cost, reproducibility, interpretability, benchmarking, and generalization. The paper concludes by outlining open research questions and future directions for developing more robust, transparent, and scalable LLM-EC systems.
The Lattice Boltzmann method (LBM) is particularly well-suited for implementation on quantum circuits owing to its simple algebraic operations and natural parallelism. However, most quantum LBMs fix τ = 1 to simplify the lattice Boltzmann equation to a linear system with respect to the equilibrium distribution function, which restricts the simulation to a fixed mesh size for a given Reynolds number. To preserve the simplicity associated with τ = 1, we propose a quantum lattice kinetic scheme (LKS). In this approach, a constant parameter A is introduced into the equilibrium distribution function (EDF), enabling independent adjustment of the fluid viscosity. This modification decouples the viscosity from the mesh size, thereby enabling simulations at varying Reynolds numbers on a fixed mesh size. The Chapman-Enskog analysis confirms that the modified EDF still recovers the Navier-Stokes equations without compromising accuracy. We evaluate the method on 2D and 3D Taylor-Green vortex and lid-driven cavity flows, demonstrating that quantum LKS attains the same accuracy and convergence order as classical LKS. The first application of quantum LBM to 3D incompressible flows represents a significant step forward in large-scale fluid dynamics simulation. All the data and code associated with this work have been made publicly available at: https://github.com/XiaoY-1012/QLKS-LBM.
The rapid development of autonomous vehicle (AV) technology is ushering in a new era of transportation characterized by the mixed-autonomy traffic (or mixed traffic for short), where AVs coexist and interact with human-driven vehicles (HVs). To harness the full potential of this transition, substantial research efforts over the past decade have been devoted to characterizing and managing the mixed traffic in road networks. This study provides a timely, comprehensive, and critical review of the state-of-the-art in mixed traffic network modeling and optimization. Specifically, we focus on studies that address human mobility in general congestible road networks, with an emphasis on macroscopic modeling or management of network-wide mixed traffic involving privately owned AVs and HVs. The literature search covers 136 academic journal articles published up to December 31, 2025. We classify the models developed in these studies into two main categories: mixed traffic analysis models and mixed traffic management & control models. Mixed traffic analysis models aim to investigate and predict how mixed traffic operates, evolves, or distributes over a network. We investigate these studies from three temporal dimensions: static, day-to-day dynamic and within-day dynamic. We examine the heterogeneity between AVs and HVs at the network level, translate it into ten AV-related behavioral assumptions, and highlight how these assumptions challenge and reshape modeling paradigms. Mixed traffic management & control models, on the other hand, focus on the determination of optimal management and control strategies. We overview cutting-edge strategies, including infrastructure retrofitting, participatory AV control, pricing and subsidization, among others. We elucidate how the strategy design problems are formulated and solved, and further discuss the application scenarios, advantages, limitations, and implementation barriers of these strategies. Finally, we identify critical research gaps and shed light on several inspiring directions for future investigation. This study aims to offer valuable insights for both academic research and practical decision-making regarding the deployment of AVs.
High-density urban environments present competing environmental exposures that jointly influence human health. Increased sky openness enhances visual quality but simultaneously intensifies thermal stress, while shading strategies that reduce heat exposure may constrain restorative benefits. Such trade-offs pose challenges for environmental assessment and urban design in dense urban settings. This study proposes a spatially explicit framework to jointly assess and optimize thermal comfort and visual restorative potential at the panorama level, integrating street-view image-based spatial metrics, XGBoost with explainable SHAP analysis, and Genetic Algorithm-based optimization, enabling interpretation of non-linear trade-offs that prior separate-domain analyses could not resolve. Using nine residential neighborhoods in Singapore, the Universal Thermal Climate Index (UTCI) and Perceived Restorativeness Scale Score (PRSS) were predicted from image-based metrics and synthesized into an Integrated Thermal-Visual Score (ITVS). SHAP analysis revealed that Sky (31.9%), Tree (20.8%), and Depth (13.0%) were the dominant predictors with non-linear patterns: Tree > 0.15 and Depth > 0.35 improved integrated performance, while Sky > 0.06 degraded it as thermal penalties outweighed visual gains. Optimization achieved a mean UTCI reduction of 0.327 °C and PRSS increase of 0.535. Cluster analysis further identified three morphological typologies, i.e., open-exposed, dense-shaded, and green-balanced, with optimization gains ranging from 22.1% to 32.5%, underscoring the need for morphology-specific strategies. These empirically derived thresholds and the integrated optimization framework offer transferable, quantitative guidance for performance-oriented urban design in high-density tropical cities.
We study a dynamic type-matching problem in mobility-on-demand (MoD) systems where multiple passenger segments have differentiated mobility requirements and multi-type vehicle fleets exhibit asymmetric service compatibilities. This general structure arises naturally whenever a specialized vehicle type is exclusively reserved for a high-priority demand segment and must be prioritized accordingly. Motivated by the mobility challenges of aging societies, we instantiate this structure in a senior-centric MoD context, incorporating Wheelchair Accessible Vehicles (WAVs) as a complementary fleet component dedicated to serving seniors with accessibility needs. The resulting type-matching problem features multi-type fleets, differentiated passenger segments, and asymmetric compatibilities, necessitating the development of heterogeneous yet interdependent fleet policies. We formulate the problem as a stochastic dynamic program that embeds accessibility and priority principles, and develop a Fleet-Decomposable Markov Game (Fleet-Dec MG) to model coordinated decisions among fleets with asymmetric roles. We establish a sequential fleet-based value decomposition property that preserves the long-run objective while respecting feasibility coupling between fleet policies. Building on this, we propose a Fleet-Decomposable Trust-Region Policy Optimization (FDTRPO) algorithm as a theoretical solution to the proposed Fleet-Dec MG. We prove that FDTRPO has monotonic improvement properties and converges to a Trust-Region Nash equilibrium. To enable scalable real-time deployment, we further develop a Fleet-Decomposable Proximal Policy Optimization (FDPPO) algorithm. Through city-scale experiments on real-world taxi data, we demonstrate that FDPPO converges smoothly in a high-dimensional, stochastic environment and yields deployable policies that enhance both efficiency and fairness. Compared with advanced learning benchmarks, the optimal FDPPO policy achieves a 2.6% increase in system profit and boosts the order fulfillment rate for senior passengers to over 90% with an improvement of nearly 9 percentage points for those with accessibility needs, while the levels of service of other segments are also improved. We also derive managerial insights for operators aiming to expand accessible and inclusive services for seniors and passengers with disabilities. Overall, this work advances a theoretically grounded yet practical paradigm for the dynamic type-matching problem with asymmetric compatibilities and interdependent fleet coordinations.
