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
The Runtime of Randomized Local Search on the Generalized Needle Problem, IEEE TEVC
https://ieeexplore.ieee.org/document/10663481
In their recent work, C. Doerr and Krejca (Transactions on Evolutionary Computation, 2023) proved upper bounds on the expected runtime of the randomized local search heuristic on generalized functions. Based on these upper bounds, they deduce in a not fully rigorous manner a drastic influence of the needle radius k on the runtime. In this short article, we add the missing lower bound necessary to determine the influence of parameter k on the runtime. To this aim, we derive an exact description of the expected runtime, which also significantly improves the upper bound given by C. Doerr and Krejca. We also describe asymptotic estimates of the expected runtime.
A Divergence-Based Condition to Ensure Quantile Improvement in Black-Box Global Optimization, IEEE TEVC
https://ieeexplore.ieee.org/document/10663075
Black-box global optimization aims at minimizing an objective function whose analytical form is not known. To do so, many state-of-the-art methods rely on sampling-based strategies, where sampling distributions are built in an iterative fashion, so that their mass concentrate where the objective function is low. Despite empirical success, the theoretical study of these methods remains difficult. In this work, we introduce a new framework, based on divergence-decrease conditions, to study and design black-box global optimization algorithms. Our approach allows to establish and quantify the improvement of sampling distributions at each iteration, in terms of expected value or quantile of the objective. We show that the information-geometric optimization approach fits within our framework, yielding a new approach for its analysis. We also establish sampling distribution improvement results for two novel algorithms, one related with the cross-entropy approach with mixture models, and another one using heavy-tailed sampling distributions.
Evolutionary Dynamics of Population Games With an Aspiration-Based Learning Rule, IEEE TNNLS
https://ieeexplore.ieee.org/document/10659735
Agents usually adjust their strategic behaviors based on their own payoff and aspiration in gaming environments. Hence, aspiration-based learning rules play an important role in the evolutionary dynamics in a population of competing agents. However, there exist different options for how to use the aspiration information for specifying the microscopic learning rules. It is also interesting to investigate under what conditions the aspiration-based learning rules can favor the emergence of cooperative behavior in population games. A new learning rule, called as “Satisfied-Cooperate, Unsatisfied-Defect”, is proposed here, which is based on aspiration. Under this learning rule, agents prefer to cooperate when their income is satisfied; otherwise, they prefer the strategy of defection. We introduce this learning rule to a population of agents playing a generalized two-person game. We, respectively, obtain the mathematical conditions in which cooperation is more abundant in finite well-mixed, infinite well-mixed, and structured populations under weak selection. Interestingly, we find that these conditions are identical, no matter whether the aspiration levels for cooperators and defectors are the same or not. Furthermore, we consider the prisoner’s dilemma game (PDG) as an example and perform numerical calculations and computer simulations. Our numerical and simulation results agree well and both support our theoretical predictions in the three different types of populations. We further find that our aspiration-based learning rule can promote cooperation more effectively than alternative aspiration-based learning rules in the PDG.
Swarm Systems as a Platform for Open-Ended Evolutionary Dynamics
https://arxiv.org/abs/2409.01469
Artificial swarm systems have been extensively studied and used in computer science, robotics, engineering and other technological fields, primarily as a platform for implementing robust distributed systems to achieve pre-defined objectives. However, such swarm systems, especially heterogeneous ones, can also be utilized as an ideal platform for creating *open-ended evolutionary dynamics* that do not converge toward pre-defined goals but keep exploring diverse possibilities and generating novel outputs indefinitely. In this article, we review Swarm Chemistry and its variants as concrete sample cases to illustrate beneficial characteristics of heterogeneous swarm systems, including the cardinality leap of design spaces, multiscale structures/behaviors and their diversity, and robust self-organization, self-repair and ecological interactions of emergent patterns, all of which serve as the driving forces for open-ended evolutionary processes. Applications to science, engineering, and art/entertainment as well as the directions of further research are also discussed.
进化优化
Knee Detection in Bayesian Multi-Objective Optimization Using Thompson Sampling, IEEE TEVC
https://ieeexplore.ieee.org/document/10667668
Real-world problems often consist of multiple conflicting objectives to be optimized simultaneously, featuring a set of Pareto-optimal solutions. Estimating the entire Pareto front can be computationally expensive, and is not always necessary, as decision makers will likely be interested only in specific regions of the Pareto front. In the absence of knowledge about the decision maker preferences, the so-called knees in the Pareto front are considered to be particularly attractive. In this paper, we propose using Thompson Sampling in the Bayesian optimization framework to estimate the location of the knee regions in a data-efficient manner. Our experimental results show that the proposed methods accurately locate the knee regions after a very small number of evaluations, providing a computationally efficient approach to single-and multi-knee detection in multi-objective optimization.
Constrained Multi-objective Optimization via Relaxations on Both Constraints and Objectives, IEEE TAI
https://ieeexplore.ieee.org/document/10665969
Since most multi-objective optimization problems in real-world applications contain constraints, constraint-handling techniques (CHTs) are necessary for a multi-objective optimizer. However, existing CHTs give no relaxation to objectives, resulting in the elimination of infeasible dominated solutions that are promising (potentially useful but inferior) for detecting feasible regions and the constrained Pareto front (CPF). To overcome this drawback, in this work, we propose an objective relaxation technique that can preserve promising by relaxing objective function values, i.e., convergence, through an adaptively adjusted relaxation factor. Further, we develop a new constrained multiobjective optimization evolutionary algorithm (CMOEA) based on relaxations on both constraints and objectives. The proposed algorithm evolves one population by the constraint relaxation technique to preserve promising infeasible solutions and the other population by both objective and constraint relaxation techniques to preserve promising infeasible dominated solutions. In this way, our method can overcome the drawback of existing CHTs. Besides, an archive update strategy is designed to maintain encountered feasible solutions by the two populations to approximate the CPF. Experiments on challenging benchmark problems and real-world problems have demonstrated the superiority or at least competitiveness of our proposed CMOEA. Moreover, to verify the generality of the objective relaxation technique, we embed it into two existing CMOEA frameworks and the results show that it can significantly improve the performance in handling challenging problems.
Solving Dynamic Multiobjective Optimization Problems via Feedback-Guided Transfer and Trend Manifold Prediction, IEEE TSMC
https://ieeexplore.ieee.org/document/10668960
Solving dynamic multiobjective optimization problems (DMOPs) is very challenging due to the requirements to respond rapidly and precisely to changes in an environment. Many prediction-and memory-based algorithms have been recently proposed for meeting these requirements. However, much useful knowledge has been ignored during the historical search process, and prediction deviations could occur, thus limiting the applicability of these methods to a variety of problems. Facing these concerns, this article proposes an evolutionary algorithm named FGTTMP based on feedback-guided transfer (FGT) and trend manifold prediction (TMP) for solving DMOPs. The FGT employs an information feedback model to extract valuable knowledge using all historical environments and then identifies excellent individuals using cluster-transfer learning. This can both accelerate convergence and introduce diversity for future environments. The TMP applies the probability-based trend prediction method to estimate the mass center of the whole population and the corresponding manifold, relying on the two previous moments. Thus, the FGT and TMP strategies combine historical knowledge with prediction techniques, synergistically leading the population in promising directions. The performance of the proposed algorithm is fully investigated and compared with eight state-of-the-art algorithms. Experimental results demonstrate that the proposed FGTTMP method can achieve better convergence and diversity on 19 various benchmark problems than the state-of-the-art algorithms.
A Cooperative Multistep Mutation Strategy for Multiobjective Optimization Problems With Deceptive Constraints, IEEE TSMC
https://ieeexplore.ieee.org/document/10654248
Constrained multiobjective optimization problems with deceptive constraints (DCMOPs) are a kind of complex optimization problems and have received some attention. For DCMOPs, the closer a solution is to the feasible region, the larger its constraint value. Moreover, multiple local infeasible regions will have different minimal constraint values according to their distances to feasible regions. Therefore, most of the existing algorithms are easy to fall into local regions, and even cannot find any feasible solution. To address DCMOPs, this article proposes a new evolutionary multitasking algorithm with a cooperative multistep mutation strategy. In this algorithm, the DCMOP is transformed into a multitasking optimization problem, in which the main task is the original DCMOP and the created auxiliary task aims to provide effective help for solving the main task. Specially, the designed cooperative multistep mutation strategy contains two contributions to solve deceptive constraints. First, a multistep mechanism is proposed, in which the individuals will use multiple different steps to generate the multiple offspring solutions along one direction, so as to expand search range to find feasible regions. Second, a cooperative mechanism between the two tasks is proposed, in which the main purpose is to provide effective and stable search directions. To be specific, an opposite solution generation method is utilized to generate the opposite solution of auxiliary population in the search space, and the direction from the auxiliary population to the main population will be formed. Combined with these two mechanisms, the proposed cooperative multistep mutation strategy can effectively improve the population diversity along the promising and stable search directions. In the experiments, the proposed algorithm is tested on the two benchmark DCMOPs, which contain objective space constraints and decision space constraints respectively. The results show the effectiveness and superiority of the proposed algorithm over the latest compared algorithms.
Efficient Sparse Large-Scale Multiobjective Optimization Based on Cross-Scale Knowledge Fusion, IEEE TSMC
https://ieeexplore.ieee.org/document/10664523
Due to the curse of dimensionality and the unknown sparsity of search spaces, evolutionary algorithms face immense challenges in approximating optimal solutions for widely studied sparse large-scale multiobjective optimization problems (SLMOPs). Most bilevel encoding scheme (BLES)-based algorithms primarily focus on exploring sparsity in the binary layer, neglecting the real layer. Moreover, the interactions between two layers may be disregarded in these algorithms, thus the latent gap between the two encoding scales could lead to evolutionary ambiguity and performance limitations. To tackle the above issues, this article proposes a novel BLES-based collaborative algorithm using cross-scale knowledge fusion for SLMOPs. The algorithm integrates dual grouping and dual dimension reduction techniques via two subpopulations in a coevolutionary manner. Additionally, the interaction strategy is designed for each technique, leveraging the binary layer to guide the real layer, thus facilitating sufficient cross-scale cooperation. Extensive experiments on benchmark SLMOPs and four real-world applications validate the proposed algorithm’s strong competitiveness in solving SLMOPs compared to state-of-the-art algorithms.
Particle Search Control Network for Dynamic Optimization, IEEE TSMC
https://ieeexplore.ieee.org/document/10664617
In dynamic optimization problems (DOPs), environmental changes can be characterized as various dynamics. Faced with different dynamics, existing dynamic optimization algorithms (DOAs) are difficult to tackle, because they are incapable of learning in each environment to control the search. Besides, diversity loss is a critical issue in solving DOPs. Maintaining a high-diversity over dynamic environments is reasonable as it can address such an issue automatically. In this article, we propose a particle search control network (PSCN) to maintain a high-diversity over time and control two key search actions of each input individual, i.e., locating the local learning target and adjusting the local acceleration coefficient. Specifically, PSCN adequately considers the diversity to generate subpopulations located by hidden node centers, where each center is assessed by significance-based criteria and distance-based criteria. The former enable a small intrasubpopulation distance and a big search scope (subpopulation width) for each subpopulation, while the latter make each center distant from other existing centers. In each subpopulation, the best-found position is selected as the local learning target. In the output layer, PSCN determines the action of adjusting the local acceleration coefficient of each individual. Reinforcement learning is introduced to obtain the desired output of PSCN, enabling the network to control the search by learning in different iterations of each environment. The experimental results especially performance comparisons with eight state-of-the-art DOAs demonstrate that PSCN brings significant improvements in performance of solving DOPs.
A Dynamic Knowledge-Guided Coevolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems, IEEE TSMC
https://ieeexplore.ieee.org/document/10664607
Large-scale sparse multiobjective optimization problems (SMOPs) exist widely in real-world applications, and solving them requires algorithms that can handle high-dimensional decision space while simultaneously discovering the sparse distribution of Pareto optimal solutions. However, it is difficult for most existing multiobjective evolutionary algorithms (MOEAs) to get satisfactory results. To address this problem, this article proposes a dynamic knowledge-guided coevolutionary algorithm, which employs a cooperative coevolutionary framework tailored for large-scale SMOPs. Specifically, variable selection is performed initially for the dimension reduction, and two populations are evolved in the original and reduced decision spaces, respectively. After offspring generation, variable replacement is performed to precisely identify the sparse distribution of Pareto optimal solutions. Furthermore, a dynamic score update mechanism is designed based on the discovered sparsity knowledge, which aims to adjust the direction of evolution dynamically. The superiority of the proposed algorithm is demonstrated by applying it to a variety of benchmark test instances and real-world test instances with the comparison of five other state-of-the-art MOEAs.
Pareto Set Prediction Assisted Bilevel Multi-objective Optimization
https://arxiv.org/abs/2409.03328
Bilevel optimization problems comprise an upper level optimization task that contains a lower level optimization task as a constraint. While there is a significant and growing literature devoted to solving bilevel problems with single objective at both levels using evolutionary computation, there is relatively scarce work done to address problems with multiple objectives (BLMOP) at both levels. For black-box BLMOPs, the existing evolutionary techniques typically utilize nested search, which in its native form consumes large number of function evaluations. In this work, we propose to reduce this expense by predicting the lower level Pareto set for a candidate upper level solution directly, instead of conducting an optimization from scratch. Such a prediction is significantly challenging for BLMOPs as it involves one-to-many mapping scenario. We resolve this bottleneck by supplementing the dataset using a helper variable and construct a neural network, which can then be trained to map the variables in a meaningful manner. Then, we embed this initialization within a bilevel optimization framework, termed Pareto set prediction assisted evolutionary bilevel multi-objective optimization (PSP-BLEMO). Systematic experiments with existing state-of-the-art methods are presented to demonstrate its benefit. The experiments show that the proposed approach is competitive across a range of problems, including both deceptive and non-deceptive problems
Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It
https://arxiv.org/abs/2409.00306
Randomized search heuristics (RHSs) are generally believed to be robust to noise. However, almost all mathematical analyses on how RSHs cope with a noisy access to the objective function assume that each solution is re-evaluated whenever it is compared to others. This is unfortunate, both because it wastes computational resources and because it requires the user to foresee that noise is present (as in a noise-free setting, one would never re-evaluate solutions). In this work, we show the need for re-evaluations could be overestimated, and in fact, detrimental. For the classic benchmark problem of how the (1+1) evolutionary algorithm optimizes the LeadingOnes benchmark, we show that without re-evaluations up to constant noise rates can be tolerated, much more than the O(n−2logn) noise rates that can be tolerated when re-evaluating solutions. This first runtime analysis of an evolutionary algorithm solving a single-objective noisy problem without re-evaluations could indicate that such algorithms cope with noise much better than previously thought, and without the need to foresee the presence of noise.
组合优化
A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode
https://arxiv.org/abs/2408.16300
With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem.
神经进化
Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification
https://arxiv.org/abs/2409.01446
In landscape-aware algorithm selection problem, the effectiveness of feature-based predictive models strongly depends on the representativeness of training data for practical applications. In this work, we investigate the potential of randomly generated functions (RGF) for the model training, which cover a much more diverse set of optimization problem classes compared to the widely-used black-box optimization benchmarking (BBOB) suite. Correspondingly, we focus on automated algorithm configuration (AAC), that is, selecting the best suited algorithm and fine-tuning its hyperparameters based on the landscape features of problem instances. Precisely, we analyze the performance of dense neural network (NN) models in handling the multi-output mixed regression and classification tasks using different training data sets, such as RGF and many-affine BBOB (MA-BBOB) functions. Based on our results on the BBOB functions in 5d and 20d, near optimal configurations can be identified using the proposed approach, which can most of the time outperform the off-the-shelf default configuration considered by practitioners with limited knowledge about AAC. Furthermore, the predicted configurations are competitive against the single best solver in many cases. Overall, configurations with better performance can be best identified by using NN models trained on a combination of RGF and MA-BBOB functions.
进化学习
Multi-Agent Evolutionary Reinforcement Learning Based on Cooperative Games, IEEE TETCI
https://ieeexplore.ieee.org/document/10666798
Despite the significant advancements in single-agent evolutionary reinforcement learning, research exploring evolutionary reinforcement learning within multi-agent systems is still in its nascent stage. The integration of evolutionary algorithms (EA) and reinforcement learning (RL) has partially mitigated RL's reliance on the environment and provided it with an ample supply of data. Nonetheless, existing studies primarily focus on the indirect collaboration between RL and EA, which lacks sufficient exploration on the effective balance of individual and team rewards. To address this problem, this study introduces game theory to establish a dynamic cooperation framework between EA and RL, and proposes a multi-agent evolutionary reinforcement learning based on cooperative games. This framework facilitates more efficient direct collaboration between RL and EA, enhancing individual rewards while ensuring the attainment of team objectives. Initially, a cooperative policy is formed through a joint network to simplify the parameters of each agent to speed up the overall training process. Subsequently, RL and EA engage in cooperative games to determine whether RL jointly optimizes the same policy based on Pareto optimal results. Lastly, through double objectives optimization, a balance between the two types of rewards is achieved, with EA focusing on team rewards and RL focusing on individual rewards. Experimental results demonstrate that the proposed algorithm outperforms its single-algorithm counterparts in terms of competitiveness.
Evolutionary Sequential Transfer Learning for Multi-Objective Feature Selection in Classification, IEEE TETCI
https://ieeexplore.ieee.org/document/10666826
Over the past decades, evolutionary multi-objective algorithms have proven their efficacy in feature selection. Nevertheless, a prevalent approach involves addressing feature selection tasks in isolation, even when these tasks share common knowledge and interdependencies. In response to this, the emerging field of evolutionary sequential transfer learning is gaining attention for feature selection. This novel approach aims to transfer and leverage knowledge gleaned by evolutionary algorithms in a source domain, applying it intelligently to enhance feature selection outcomes in a target domain. Despite its promising potential to exploit shared insights, the adoption of this transfer learning paradigm for feature selection remains surprisingly limited due to the computational expense of existing methods, which learn a mapping between the source and target search spaces. This paper introduces an advanced multi-objective feature selection approach grounded in evolutionary sequential transfer learning, strategically crafted to tackle interconnected feature selection tasks with overlapping features. Our novel framework integrates probabilistic models to capture high-order information within feature selection solutions, successfully tackling the challenges of extracting and preserving knowledge from the source domain without an expensive cost. It also provides a better way to transfer the source knowledge when the feature spaces of the source and target domains diverge. We evaluate our proposed method against four prominent single-task feature selection approaches and a cutting-edge evolutionary transfer learning feature selection method. Through empirical evaluation, our proposed approach showcases superior performance across the majority of datasets, surpassing the effectiveness of the compared methods.
SMEM: A Subspace Merging Based Evolutionary Method for High-Dimensional Feature Selection, IEEE TETCI
https://ieeexplore.ieee.org/document/10665901
In the past decade, evolutionary algorithms (EAs) have shown their promising performance in solving the problem of feature selection. Despite that, it is still quite challenging to design the EAs for high-dimensional feature selection (HDFS), since the increasing number of features causes the search space of EAs grows exponentially, which is known as the “curse of dimensionality”. To tackle the issue, in this paper, a Subspace Merging based Evolutionary Method, termed SMEM is suggested. In SMEM, to avoid directly optimizing the large search space of HDFS, the original feature space of HDFS is firstly divided into several independent low-dimensional subspaces. In each subspace, a subpopulation is evolved to obtain the latent good feature subsets quickly. Then, to avoid some features being missed, these low-dimensional subspaces merge in pairs, and the further search is carried on the merged subspaces. During the evolving of each merged subspace, the good feature subsets obtained from previous subspace pair are fully utilized. The above subspace merging procedure repeats, and the performance of SMEM is improved gradually, until in the end, all the subspaces are merged into one final space. At that time, the final space is also the original feature space in HDFS, which ensures all the features in the data is considered. Experimental results on different high-dimensional datasets demonstrate the effectiveness and the efficiency of the proposed SMEM, when compared with the state-of-the-arts.
Sparse Learning-Based Feature Selection in Classification: A Multi-Objective Perspective, IEEE TETCI
https://ieeexplore.ieee.org/document/10663218
Sparse learning-based feature selection is an emerging topic, acclaimed for its potential in delivering promising performance and interpretability. Nevertheless, the task of determining a suitable regularization parameter to strike a balance between the loss function and regularization is a challenging endeavor, where existing methods encounter great difficulties. Moreover, the ranking mechanism in most sparse learning-based feature selection methods requires a predefined number of selected features, which is usually dataset-dependent and not known in advance. It is of great importance to automatically balance the loss function and sparse regularization and determine the appropriate number of selected features. To this end, this paper proposes formulating the sparse learning-based feature selection problem as a bi-objective optimization problem, which takes the loss term and the ℓ2,0 -norm regularization as two objectives, to automatically identify the optimal number of selected features and obtain a set of trade-off solutions between the loss term and the number of selected features. To solve such a non-convex problem, a novel solution representation, an initialization strategy, and an environmental selection operator are proposed. Compared with seven feature selection methods, extensive experiments on 16 practical classification datasets demonstrate that the proposed method attains highly competitive classification accuracy with a small number of selected features, and the features selected by the proposed method have low redundancy.
Language Model Crossover: Variation through Few-Shot Prompting, ACM TELO
https://dl.acm.org/doi/10.1145/3694791
This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models of sufficient scale demonstrate in-context learning, i.e. they can learn from associations between a small number of input patterns to generate outputs incorporating such associations (also called few-shot prompting). This ability can be leveraged to form a simple but powerful variation operator, i.e. to prompt a language model with a few text-based genotypes (such as code, plain-text sentences, or equations), and to parse its corresponding output as those genotypes’ offspring. The promise of such language model crossover (which is simple to implement and can leverage many different open-source language models) is that it enables a simple mechanism to evolve semantically-rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in language models. Experiments in this paper highlight the versatility of language-model crossover, through evolving binary bit-strings, sentences, equations, text-to-image prompts, and Python code. The conclusion is that language model crossover is a flexible and effective method for evolving genomes representable as text.
应用研究
A Local-Search-Based Heuristic for Coalition Formation in Urgent Missions, IEEE TSMC
https://ieeexplore.ieee.org/document/10663079
This article focuses on the coalition formation (CF) problem in urgent missions, e.g., disaster rescue, where coalition members should reach mission locations quickly. A mathematical model is first constructed to minimize the latest arrival time of coalition members, considering the capability requirements of missions, nonredundant agents in coalitions, etc. Then, incorporating the benefits in both the diversity of random search and the effectiveness of utilizing problem knowledge, a local-search-based heuristic is put forward to solve the CF problem. An initial solution is incrementally constructed by prioritizing agents with shorter movement times for missions with higher-remaining capability requirements. Additionally, two types of neighborhood search operators, namely, the tabu-based one-to-one swap and the destroy and repair operators, are proposed to search the solution space from two perspectives, i.e.“, adjustment” and “reconstruction.” To solve the problem effectively and efficiently, the former excludes certain agent-exchange combinations that do not improve the current solution, while the latter consists of multiple heuristic rules extracted from the correlation among different model elements. Experimental results have demonstrated that the proposed method surpasses several advanced methods across various scenarios regarding multiple factors, such as the number of agents, the number of missions, and the demand–supply ratio on capabilities.
A Diversified Population Migration-Based Multiobjective Evolutionary Algorithm for Dynamic Community Detection, IEEE TETCI
https://ieeexplore.ieee.org/document/10666834
Dynamic community detection, which is capable of revealing changes in community structure over time, has garnered increasing attention in research. While evolutionary clustering methods have proven to be effective in tackling this issue, they often have a tendency to favor what are referred to as elite solutions, inadvertently neglecting the potential value of non-elite alternatives. Although elite solutions can ensure population convergence, they may result in negative population migration due to the lack of diversity when the network changes. In contrast, when the network undergoes changes, non-elite solutions could better adapt to the changed network, thereby can help the algorithm find accurate community structures in the new environment. To this end, we propose a diversified population migration strategy that consists of two-stages, i.e., solution selection and solution migration. In the first stage, we use elite solutions not only to ensure convergence but also non-elite solutions to maintain diversity and cope with network changes. In the second stage, the migration solutions are refined by using incremental changes between the two consecutive snapshots of networks. Based on the proposed strategy, we suggest a diversified population migration-based multiobjective evolutionary algorithm named DPMOEA. In DPMOEA, we design new genetic operators that utilize incremental changes between networks to make the population evolve in the right direction. Our experimental results demonstrate that the proposed method outperforms state-of-the-art baseline algorithms and can effectively solve the dynamic community detection problem.
