Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference最新文献

Comparing the expressive power of Strongly-Typed and Grammar-Guided Genetic Programming 比较强类型和语法引导遗传规划的表达能力

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590507

Alcides Fonseca, Diogo Poças

{"title":"Comparing the expressive power of Strongly-Typed and Grammar-Guided Genetic Programming","authors":"Alcides Fonseca, Diogo Poças","doi":"10.1145/3583131.3590507","DOIUrl":"https://doi.org/10.1145/3583131.3590507","url":null,"abstract":"Since Genetic Programming (GP) has been proposed, several flavors of GP have arisen, each with their own strengths and limitations. Grammar-Guided and Strongly-Typed GP (GGGP and STGP, respectively) are two popular flavors that have the advantage of allowing the practitioner to impose syntactic and semantic restrictions on the generated programs. GGGP makes use of (traditionally context-free) grammars to restrict the generation of (and the application of genetic operators on) individuals. By guiding this generation according to a grammar, i.e. a set of rules, GGGP improves performance by searching for an good-enough solution on a subset of the search space. This approach has been extended with Attribute Grammars to encode semantic restrictions, while Context-Free Grammars would only encode syntactic restrictions. STGP is also able to restrict the shape of the generated programs using a very simple grammar together with a type system. In this work, we address the question of which approach has more expressive power. We demonstrate that STGP has higher expressive power than Context-Free GGGP and less expressive power than Attribute Grammatical Evolution.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90342894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MOEAs Are Stuck in a Different Area at a Time moea在同一时间被困在不同的区域

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590447

M. Li, Xiao Han, Xiaochen Chu

{"title":"MOEAs Are Stuck in a Different Area at a Time","authors":"M. Li, Xiao Han, Xiaochen Chu","doi":"10.1145/3583131.3590447","DOIUrl":"https://doi.org/10.1145/3583131.3590447","url":null,"abstract":"In this paper, we show that when dealing with multi-objective combinatorial optimisation problems, the search, in different executions of a multi-objective evolutionary algorithm (MOEA), e.g., NSGA-II, tends to stagnate in different areas in the search space. In other words, the final populations obtained by an MOEA under multiple executions, which can be very close in the objective space, are located far away from each other in the search space. This phenomenon becomes more apparent with the increase of some type of problem complexity (e.g., the ruggedness level of problem landscape). Interestingly, the phenomenon only happens to combinatorial optimisation problems, but not to continuous ones. In this study, we consider three well-established MOEAs (NSGA-II, SMS-EMOA and MOEA/D) on two representative combinatorial optimisation problems (NK-landscape and TSP) and on two commonly used continuous problem suites (DTLZ and WFG). Experimental results show a clear difference between multi-objective combinatorial and continuous problems and suggest a need of more efforts to be put on developing effective MOEAs for combinatorial problems.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85364539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Producing Diverse Rashomon Sets of Counterfactual Explanations with Niching Particle Swarm Optimization Algorithms 用小生境粒子群优化算法产生不同的罗生门反事实解释集

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590444

Hayden Andersen, Andrew Lensen, Will N. Browne, Yi Mei

{"title":"Producing Diverse Rashomon Sets of Counterfactual Explanations with Niching Particle Swarm Optimization Algorithms","authors":"Hayden Andersen, Andrew Lensen, Will N. Browne, Yi Mei","doi":"10.1145/3583131.3590444","DOIUrl":"https://doi.org/10.1145/3583131.3590444","url":null,"abstract":"Counterfactual explanation is a popular eXplainable AI technique, that gives contrastive explanations to answer potential \"what-if\" questions about the workings of machine learning models. However, research into how explanations are understood by human beings has shown that an optimal explanation should be both selected and social, providing multiple varying explanations for the same event that allow a user to select specific explanations based on prior beliefs and cognitive biases. In order to provide such explanations, a Rashomon set of explanations can be created: a set of explanations utilising different features in the data. Current work to generate counterfactual explanations does not take this need into account, only focusing on producing a single optimal counterfactual. This work presents a novel method for generating a diverse Rashomon set of counterfactual explanations using the final population from a Particle Swarm Optimisation (PSO) algorithm. It explores a selection of PSO niching algorithms for PSO and evaluates the best algorithm to produce these sets. Finally, the ability of this method to be implemented and trusted by users is discussed.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77172049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness 具有压缩、增强可视化和表达性的Pareto局部最优解网络

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590474

A. Liefooghe, G. Ochoa, S. Vérel, B. Derbel

{"title":"Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness","authors":"A. Liefooghe, G. Ochoa, S. Vérel, B. Derbel","doi":"10.1145/3583131.3590474","DOIUrl":"https://doi.org/10.1145/3583131.3590474","url":null,"abstract":"The structure of local optima in multi-objective combinatorial optimization and their impact on algorithm performance are not yet properly understood. In this paper, we are interested in the representation of multi-objective landscapes and their multi-modality. More specifically, we revise and extend the network of Pareto local optimal solutions (PLOS-net), inspired by the well-established local optima network from single-objective optimization. We first define a compressed PLOS-net which allows us to enhance its perception while preserving the important notion of connectedness between local optima. We then study an alternative visualization of the (compressed) PLOS-net that focuses on good-quality solutions, improves the distinction between connected components in the network, and generalizes well to landscapes with more than 2 objectives. We finally define a number of network metrics that characterize the PLOS-net, some of them being strongly correlated with search performance. We visualize and experiment with small-size multiobjective nk-landscapes, and we disclose the effect of PLOS-net metrics against well-established multi-objective local search and evolutionary algorithms.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75716714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biological insights on grammar-structured mutations improve fitness and diversity 语法结构突变的生物学见解提高了适应性和多样性

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590472

Stefano Tiso, P. Carvalho, Nuno Lourenço, Penousal Machado

{"title":"Biological insights on grammar-structured mutations improve fitness and diversity","authors":"Stefano Tiso, P. Carvalho, Nuno Lourenço, Penousal Machado","doi":"10.1145/3583131.3590472","DOIUrl":"https://doi.org/10.1145/3583131.3590472","url":null,"abstract":"Grammar-Guided Genetic Programming (GGGP) employs a variety of concepts from evolutionary theory to autonomously design solutions for a given task. Recent insights from evolutionary biology can lead to further improvements in GGGP algorithms. In this paper, we propose a new mutation approach called Facilitated Mutation (FM) that is based on the theory of Facilitated Variation. We evaluate the performance of FM on the evolution of neural network optimizers for image classification, a relevant task in Evolutionary Computation, with important implications for the field of Machine Learning. We compare FM and FM combined with crossover (FMX) against a typical mutation approach to assess the benefits of the approach. We find that FMX provides statistical improvements in key metrics, creating a superior optimizer overall (+0.5% average test accuracy), improving the average quality of solutions (+53% average population fitness), and discovering more diverse high-quality behaviors (+523 high-quality solutions discovered on average). Additionally, FM and FMX reduce the number of fitness evaluations in an evolutionary run, reducing computational costs. FM's implementation cost is minimal and the approach is theoretically applicable to any algorithm where genes are associated witha grammar non-terminal, making this approach applicable in many existing GGGP systems.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85395387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Use of Second Order Neighbors to Escape from Local Optima 利用二阶邻域逃避局部最优

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590473

Manuel Torralbo, Leticia Hernando, Ernesto Contreras-Torres, J. A. Lozano

{"title":"On the Use of Second Order Neighbors to Escape from Local Optima","authors":"Manuel Torralbo, Leticia Hernando, Ernesto Contreras-Torres, J. A. Lozano","doi":"10.1145/3583131.3590473","DOIUrl":"https://doi.org/10.1145/3583131.3590473","url":null,"abstract":"Designing efficient local search based algorithms requires to consider the specific properties of the problems. We introduce a simple and efficient strategy, the Extended Reach, that escapes from local optima obtained from a best improvement local search and apply it to the linear ordering problem (LOP), the traveling salesperson problem (TSP) and the quadratic assignment problem (QAP). This strategy is based on two landscape properties observed in the literature. First, it considers that a local optimum is usually located in the frontier of its own attraction basin, and thus, it is enough to inspect the second order neighbors to reach a (better) solution inside an attraction basin of a better local optimum. Second, taking into account that for the LOP and specific neighborhoods it is possible to discard solutions without the need of being evaluated, we extend this result to the TSP with the 2-opt neighborhood to avoid the unnecessary evaluation of solutions. Efficient ways of evaluating the second order neighbors are also presented, based on the cost differences, reducing significantly the computation cost. Experimental results on random and benchmark instances show that our strategy, indeed, escapes from local optima despite its simplicity.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75113231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast Evolutionary Neural Architecture Search by Contrastive Predictor with Linear Regions 基于线性区域对比预测器的快速进化神经结构搜索

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590452

Yameng Peng, A. Song, V. Ciesielski, Haytham M. Fayek, Xiaojun Chang

{"title":"Fast Evolutionary Neural Architecture Search by Contrastive Predictor with Linear Regions","authors":"Yameng Peng, A. Song, V. Ciesielski, Haytham M. Fayek, Xiaojun Chang","doi":"10.1145/3583131.3590452","DOIUrl":"https://doi.org/10.1145/3583131.3590452","url":null,"abstract":"Evolutionary neural architecture search (ENAS) has emerged as a promising approach to finding high-performance neural architectures. However, widespread application has been limited by the expensive computational costs due to the nature of evolutionary algorithms. In this study, we aim to significantly reduce the computational costs of ENAS by involving a training-free performance metric. Specifically, the network performance can be estimated by the training-free metric with only a single forward pass. However, training-free metrics have their own challenges, in particular, an insufficient correlation with ground-truth performance. We adopt a Graph Convolutional Network (GCN) based contrastive predictor which can leverage the low cost of the training-free performance metric yet improve the correlation between the estimated performance and the true performance of the candidate architectures. Combining a training-free metric - the number of linear regions with the GCN-based contrastive predictor and an active learning scheme, we propose Fast-ENAS which can achieve superior search efficiency and performance on the benchmark NAS-Bench-201 and DARTS search spaces. Furthermore, with a single GPU searching on the DARTS space, Fast-ENAS requires only 0.02 (29 minutes) and 0.026 (37 minutes) GPU days to achieve test error rates of 2.50% and 24.30% on CIFAR-10 and ImageNet respectively.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75148657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolutionary Mixed-Integer Optimization with Explicit Constraints 具有显式约束的进化混合整数优化

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590467

Yuan Hong, D. Arnold

引用次数: 0

Evolutionary Multi-Objective Deep Reinforcement Learning for Autonomous UAV Navigation in Large-Scale Complex Environments 大规模复杂环境下自主无人机导航的进化多目标深度强化学习

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590446

Guangyan An, Ziyu Wu, Zhilong Shen, Ke Shang, H. Ishibuchi

{"title":"Evolutionary Multi-Objective Deep Reinforcement Learning for Autonomous UAV Navigation in Large-Scale Complex Environments","authors":"Guangyan An, Ziyu Wu, Zhilong Shen, Ke Shang, H. Ishibuchi","doi":"10.1145/3583131.3590446","DOIUrl":"https://doi.org/10.1145/3583131.3590446","url":null,"abstract":"Autonomous navigation of Unmanned Aerial Vehicles (UAVs) in large-scale complex environments presents a significant challenge in modern aerospace engineering, as it requires effective decision-making in an environment with limited sensing capacity, dynamic changes, and dense obstacles. Reinforcement Learning (RL) has been applied in sequential control problems, but the manual setting of hyperparameters, including reward functions, often results in suboptimal solutions and inadequate training. To address these limitations, we propose a framework that combines Multi-Objective Evolutionary Algorithms (MOEAs) with RL algorithms. The proposed framework generates a set of non-dominating parameters for the reward function using MOEAs, leading to diverse decision-making preferences, efficient convergence, and improved performance. The framework was tested on the autonomous navigation of UAVs and demonstrated significant improvement compared to traditional RL methods. This work offers a novel perspective on the problem of autonomous UAV navigation in large-scale complex environments and highlights the potential for further improvement through the integration of RL and MOEAs.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90474376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging problem-independent hyper-heuristics for real-world test laboratory scheduling 利用问题独立的超启发式方法进行实际测试实验室调度

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-15 DOI: 10.1145/3583131.3590354

Florian Mischek, Nysret Musliu

{"title":"Leveraging problem-independent hyper-heuristics for real-world test laboratory scheduling","authors":"Florian Mischek, Nysret Musliu","doi":"10.1145/3583131.3590354","DOIUrl":"https://doi.org/10.1145/3583131.3590354","url":null,"abstract":"The area of project scheduling problems has seen a tremendous amount of different problem variations. Traditionally, each problem variant requires custom solution approaches in order to produce high-quality solutions. Developing and tuning these methods is an expensive process that may have to be repeated as soon as the requirements or problem structures change. On the other hand, research into hyper-heuristics has produced general heuristic problem-solving techniques that were developed to achieve good results on multiple diverse problem domains. They work with a set of comparatively simple low-level heuristics and dynamically adapt themselves to each new problem variant. In this paper, we investigate hyper-heuristic approaches for a real-world industrial test laboratory scheduling problem and develop a new problem domain for the HyFlex hyper-heuristic framework. We propose a diverse portfolio of low-level heuristics that can be dynamically selected during the search process by hyper-heuristics to solve the problem. We evaluate and compare the performance of several problem-independent hyper-heuristics on this domain and show that they are able to match, and sometimes even exceed, the performance of state-of-the-art solution techniques that were developed and tuned specifically for this problem.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89467098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0