进化计算领域文章更新主要包括以下六大方向如下：
基础理论（包括遗传算法、进化策略、遗传编程、群智能等算法设计、理论研究、基准测试、进化思想、算法软件、综述等）
进化优化（包括黑盒优化、多目标优化、约束优化、噪声优化、多任务优化、多模态优化、迁移优化、大规模优化、昂贵优化、学习优化等）
组合优化（包括进化神经组合优化、进化机器人、路线规划、布局布线、工业控制、调度等）
神经进化（包含进化神经网络的参数、超参数、架构、规则等）
进化学习（包括进化特征选择、强化学习、多目标学习、公平性学习、联邦学习、进化计算机视觉、进化自然语言处理、进化数据挖掘等）
应用研究（工业、网络、安全、物理、生物、化学等）
文章来源主要包括：
1. IEEE CIS: CIM, TEVC, TNNLS, TFS, TAI, TETCI, CEC
2. IEEE CS/SMC: TPAMI, TKDE, TPDS, TCYB, TSMC, Proc. IEEE
3. ACM: TELO, GECCO, FOGA, ICML
4. MIT: ECJ, ARTL, JMLR, NIPS
5. Elsevier/Springer: AIJ, SWEVO, SCIS, PPSN
6. AAAI/MK/OR: AAAI, IJCAI, ICLR
7. Else: NMI, NC, PNAS, Nature, Science, ArXiv
基础理论
Quantum Acceleration of Black-Box Pseudo-Boolean Optimization Algorithms, IEEE TEVC
https://ieeexplore.ieee.org/document/10730757
The (1 + 1) Quantum evolutionary algorithm (1 + 1) QEA uses quantum probability amplification to accelerate the classical (1 + 1) Evolutionary algorithm (1 + 1) EA for the optimization of pseudo-Boolean functions, which assign real-valued fitness to binary strings. However, determining the optimal mutation rate remains crucial to reducing the required number of function evaluations. To address this, we introduce a dynamic mutation rate strategy for the (1 + 1) QEA, leveraging the guarantees the Fixed-point Amplitude Amplification algorithm gives to adjust the mutation rate dynamically (1 + 1) QEAdyn. We derive upper bounds on the number of fitness evaluations the strategy requires to solve well-known benchmark functions. Notably, we close the performance gap of the (1 + 1) QEA on the NEEDLE problem, matching the optimal performance possible in the quantum setting. Nevertheless, our results on ONEMAX and LEADINGONES lag behind known classical results. So, we delve into quantum black-box complexity, exploring the difficulty of problem-solving on a quantum computer without explicit problem knowledge. We introduce a quantum algorithm that solves the n-dimensional LEADINGONES problem with at most n evaluations. Additionally, we develop a quantum algorithm for the ONEMAX problem, requiring only one function evaluation. Both of these quantum algorithms surpass the capabilities of classical algorithms. Finally, we devise a quantum algorithm for solving the ONEMAX problem using only fitness comparisons and provide computational evidence that it still does so faster than any classical algorithm.
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems, IEEE TEVC
https://ieeexplore.ieee.org/document/10730793
Artificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust. In response, the field of explainable AI (XAI) has emerged to address the need for human-understandable AI systems. Evolutionary computation (EC), a family of powerful optimization and learning algorithms, offers significant potential to contribute to XAI, and vice versa. This paper provides an introduction to XAI and reviews current techniques for explaining machine learning models. We then explore how EC can be leveraged in XAI and examine existing XAI approaches that incorporate EC techniques. Furthermore, we discuss the application of XAI principles within EC itself, investigating how these principles can illuminate the behavior and outcomes of EC algorithms, their (automatic) configuration, and the underlying problem landscapes they optimize. Finally, we discuss open challenges in XAI and highlight opportunities for future research at the intersection of XAI and EC. Our goal is to demonstrate EC’s suitability for addressing current explainability challenges and to encourage further exploration of these methods, ultimately contributing to the development of more understandable and trustworthy ML models and EC algorithms.
进化优化
Comparative Analysis of Indicators for Multiobjective Diversity Optimization
https://arxiv.org/abs/2410.18900
Indicator-based (multiobjective) diversity optimization aims at finding a set of near (Pareto-)optimal solutions that maximizes a diversity indicator, where diversity is typically interpreted as the number of essentially different solutions. Whereas, in the first diversity-oriented evolutionary multiobjective optimization algorithm, the NOAH algorithm by Ulrich and Thiele, the Solow Polasky Diversity (also related to Magnitude) served as a metric, other diversity indicators might be considered, such as the parameter-free Max-Min Diversity, and the Riesz s-Energy, which features uniformly distributed solution sets. In this paper, focusing on multiobjective diversity optimization, we discuss different diversity indicators from the perspective of indicator-based evolutionary algorithms (IBEA) with multiple objectives. We examine theoretical, computational, and practical properties of these indicators, such as monotonicity in species, twinning, monotonicity in distance, strict monotonicity in distance, uniformity of maximizing point sets, computational effort for a set of size~n, single-point contributions, subset selection, and submodularity. We present new theorems -- including a proof of the NP-hardness of the Riesz s-Energy Subset Selection Problem -- and consolidate existing results from the literature. In the second part, we apply these indicators in the NOAH algorithm and analyze search dynamics through an example. We examine how optimizing with one indicator affects the performance of others and propose NOAH adaptations specific to the Max-Min indicator.
组合优化
Knowledge Transfer Driven Distributed Memetic Architecture and Algorithm for Distributed Differentiation Flowshop Integrated Scheduling, IEEE TEVC
https://ieeexplore.ieee.org/document/10734404
Distributed manufacturing and fine-manufacturing are two typical scenarios of modern manufacturing industries in the context of globalization and customization. The distributed differentiation flowshop integrated scheduling problem (DDFISP) is a novel model that deals with the integrated scheduling problem of these two manufacturing scenarios. In the DDFISP, jobs have multiple customized types and are manufactured in a number of distributed factories. Each factory includes three fine-processing stages: parallel machine fabrication, single machine assembly, and dedicated machine differentiation. In the paper, a new distributed memetic evolutionary architecture is first built, which consists of four modules with distinct functions, including global exploration, local exploitation, knowledge transfer, and search restart. The exploration and exploitation are coevolved in the distributed way and communicated by knowledge transfer. This architecture can be used as a universal model to construct evolutionary algorithms. Following this architecture and devising each module innovatively, a novel knowledge transfer-driven distributed memetic algorithm (KTDMA) is constructed to solve the DDFISP. Specifically, global exploration is performed on multiple populations by dynamically selecting global exploration optimizers from predefined external repository. Local exploitation is executed on an independent elite archive by a destruction-construction local search and a key block local search. Knowledge transfer is conducted to communicate the superior information between exploration and exploitation based on a point-ring topology. Search restart is adaptively carried out to alleviate the search homogeneity. Computational results show the effectiveness of the proposed evolutionary architecture and special designs, and demonstrate that the KTDMA performs more competitive than the compared state-of-the-art algorithms.
A practical applicable quantum-classical hybrid ant colony algorithm for the NISQ era
https://arxiv.org/abs/2410.17277
Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithm overcoming some limitations of the traditional ACO algorithm. However,due to the hardware resource limitations of currently available quantum computers, the practical application of the QACO is still not realized. In this paper, we developed a quantum-classical hybrid algorithm by combining the clustering algorithm with QACO this http URL extended QACO can handle large-scale optimization problems with currently available quantum computing resource. We have tested the effectiveness and performance of the extended QACO algorithm with the Travelling Salesman Problem (TSP) as benchmarks, and found the algorithm achieves better performance under multiple diverse datasets. In addition, we investigated the noise impact on the extended QACO and evaluated its operation possibility on current available noisy intermediate scale quantum(NISQ) devices. Our work shows that the combination of the clustering algorithm with QACO effectively improved its problem solving scale, which makes its practical application possible in current NISQ era of quantum computing.
Green vehicle routing problem that jointly optimizes delivery speed and routing based on the characteristics of electric vehicles
https://arxiv.org/abs/2410.14691
The abundance of materials and the development of the economy have led to the flourishing of the logistics industry, but have also caused certain pollution. The research on GVRP (Green vehicle routing problem) for planning vehicle routes during transportation to reduce pollution is also increasingly developing. Further exploration is needed on how to integrate these research findings with real vehicles. This paper establishes an energy consumption model using real electric vehicles, fully considering the physical characteristics of each component of the vehicle. To avoid the distortion of energy consumption models affecting the results of route planning. The energy consumption model also incorporates the effects of vehicle start/stop, speed, distance, and load on energy consumption. In addition, a load first speed optimization algorithm was proposed, which selects the most suitable speed between every two delivery points while planning the route. In order to further reduce energy consumption while meeting the time window. Finally, an improved Adaptive Genetic Algorithm is used to solve for the most energy-efficient route. The experiment shows that the results of using this speed optimization algorithm are generally more energy-efficient than those without using this algorithm. The average energy consumption of constant speed delivery at different speeds is 17.16% higher than that after speed optimization. Provided a method that is closer to reality and easier for logistics companies to use. It also enriches the GVRP model.
神经演化
Spiking Neural Networks as a Controller for Emergent Swarm Agents
https://arxiv.org/abs/2410.16175
Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to ``organically" create behaviors which do not need expensive sensors and perception. Existing research explores the possible emergent behaviors in swarms of robots with only a binary sensor and a simple but hand-picked controller structure. Even agents in this highly limited sensing, actuation, and computational capability class can exhibit relatively complex global behaviors such as aggregation, milling, and dispersal, but finding the local interaction rules that enable more collective behaviors remains a significant challenge. This paper investigates the feasibility of training spiking neural networks to find those local interaction rules that result in particular emergent behaviors. In this paper, we focus on simulating a specific milling behavior already known to be producible using very simple binary sensing and acting agents. To do this, we use evolutionary algorithms to evolve not only the parameters (the weights, biases, and delays) of a spiking neural network, but also its structure. To create a baseline, we also show an evolutionary search strategy over the parameters for the incumbent hand-picked binary controller structure. Our simulations show that spiking neural networks can be evolved in binary sensing agents to form a mill.
"Efficient Complexity": a Constrained Optimization Approach to the Evolution of Natural Intelligence
https://arxiv.org/abs/2410.13881
A fundamental question in the conjunction of information theory, biophysics, bioinformatics and thermodynamics relates to the principles and processes that guide the development of natural intelligence in natural environments where information about external stimuli may not be available at prior. A novel approach in the description of the information processes of natural learning is proposed in the framework of constrained optimization, where the objective function represented by the information entropy of the internal states of the system with the states of the external environment is maximized under the natural constraints of memory, computing power, energy and other essential resources. The progress of natural intelligence can be interpreted in this framework as a strategy of approximation of the solutions of the optimization problem via a traversal over the extrema network of the objective function under the natural constraints that were examined and described. Non-trivial conclusions on the relationships between the complexity, variability and efficiency of the structure, or architecture of learning models made on the basis of the proposed formalism can explain the effectiveness of neural networks as collaborative groups of small intelligent units in biological and artificial intelligence.
进化学习
Heterogeneous Multi-Agent Zero-Shot Coordination by Coevolution, IEEE TEVC
https://ieeexplore.ieee.org/document/10730789
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some studies have made progress in ZSC by exposing the agents to diverse partners during the training process. They usually involve self-play when training the partners, implicitly assuming that the tasks are homogeneous. However, many real-world tasks are heterogeneous, and hence previous methods may be inefficient. In this paper, we study the heterogeneous ZSC problem for the first time and propose a general method based on coevolution, which coevolves two populations of agents and partners through three sub-processes: pairing, updating and selection. Experimental results on various heterogeneous tasks highlight the necessity of considering the heterogeneous setting and demonstrate that our proposed method is a promising solution for heterogeneous ZSC tasks. To the best of our knowledge, we are the first to underscore the significance of the heterogeneous ZSC tasks and to introduce an effective framework for addressing it.
Moral Preferences Co-Evolve With Cooperation in Networked Populations, IEEE TEVC
https://ieeexplore.ieee.org/document/10735402
Unravelling the evolution of cooperation is essential for advancing natural and artificial intelligence (AI) systems. Previous studies have investigated the impact of additional incentives, such as reciprocity and reputation, on cooperative behaviour. However, a fundamental question persists: under what conditions do moral preferences evolve, and does this evolution subsequently promote cooperation in networked populations of agents? To address this question, we propose a comprehensive framework to systematically explore the co-evolution of moral preferences and cooperative behaviour in a networked population. In our framework, the population structure is modelled as a network, with nodes corresponding to AI agents. Moral preferences are modelled through a learning algorithm that adheres to social norms. Prosocial and antisocial behaviours lead to rewards or punishments, and learning agents receive morality scores based on their rewarding behaviour towards others. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm in a networked population, showcasing faster convergence. We find that moral preferences enhance cooperation as long as the learning rate is moderate, even in the presence of dominant defectors. This surprising finding also holds for cooperation-inhibiting network structures, provided the critical benefit-cost ratio for cooperation is sufficiently high or below average. Interestingly, moral preferences also co-evolve with cooperation in the populations. Our work not only provides new design methodologies for network algorithms, but also highlights the insight that large-scale evolutionary computation can provide for evolutionary biology and emerging AI-agent populations.
In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics
https://arxiv.org/abs/2410.16309
Large Language Models (LLMs) have shown great potential in automatically generating and optimizing (meta)heuristics, making them valuable tools in heuristic optimization tasks. However, LLMs are generally inefficient when it comes to fine-tuning hyper-parameters of the generated algorithms, often requiring excessive queries that lead to high computational and financial costs. This paper presents a novel hybrid approach, LLaMEA-HPO, which integrates the open source LLaMEA (Large Language Model Evolutionary Algorithm) framework with a Hyper-Parameter Optimization (HPO) procedure in the loop. By offloading hyper-parameter tuning to an HPO procedure, the LLaMEA-HPO framework allows the LLM to focus on generating novel algorithmic structures, reducing the number of required LLM queries and improving the overall efficiency of the optimization process. We empirically validate the proposed hybrid framework on benchmark problems, including Online Bin Packing, Black-Box Optimization, and the Traveling Salesperson Problem. Our results demonstrate that LLaMEA-HPO achieves superior or comparable performance compared to existing LLM-driven frameworks while significantly reducing computational costs. This work highlights the importance of separating algorithmic innovation and structural code search from parameter tuning in LLM-driven code optimization and offers a scalable approach to improve the efficiency and effectiveness of LLM-based code generation.
应用研究
A Genetic Algorithm Based on Deep Q-learning in Optimization of Remote Sensing Data Discretization, IEEE TEVC
https://ieeexplore.ieee.org/document/10730790
Feature discretization can improve the processing efficiency of remote sensing big data. However, the distribution of target attribute values is often difficult to ascertain, and there are complex correlations between features, making it extremely difficult to obtain an optimal discretization scheme for remote sensing data. Although feature discretization methods based on evolutionary models can achieve some considerable results, it is difficult to formulate appropriate strategies without prior knowledge as guidance, which makes searching in multidimensional space inefficient and prone to falling into local optima. Therefore, we propose a genetic algorithm based on deep Q-learning (DQLGA) to optimize the discretization scheme of remote sensing data. First, we design a state set in the crossover and mutation stages by balancing the global and local search capabilities of genetic operators to obtain an accurate mapping of states in high-dimensional feature space. Then, we simulate the variation pattern of the optimal discretization scheme by analyzing the distribution of all individuals in the population during the evolution process to construct a reasonable adaptive reward function in reinforcement learning model. Finally, we introduce a pair of deep Q-networks with the same structure by treating the calculation of Q-values as a function fitting problem to fulfil an efficient updating of massive Q-values in state transition. We compare DQLGA with state-of-the-art feature discretization methods on remote sensing data. The experimental results indicate that DQLGA significantly improves the search efficiency of feature discretization methods based on evolutionary models, achieving higher classification accuracy while further reducing the number of breakpoints.
