Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference最新文献_第10页

Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendIt 一对优势协同进化算法分析及防御

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590411

P. Lehre, Mario Alejandro Hevia Fajardo, J. Toutouh, Erik Hemberg, Una-May O’Reilly

引用次数: 0

On the evolution of mechanisms for three-option collective decision-making in a swarm of simulated robots 模拟机器人群中三选项集体决策机制的演化

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590385

Ahmed Almansoori, M. Alkilabi, E. Tuci

{"title":"On the evolution of mechanisms for three-option collective decision-making in a swarm of simulated robots","authors":"Ahmed Almansoori, M. Alkilabi, E. Tuci","doi":"10.1145/3583131.3590385","DOIUrl":"https://doi.org/10.1145/3583131.3590385","url":null,"abstract":"To act cohesively as a group, robot swarms must be able to make decisions collectively. Collective decision-making refers to a process in which once a group decision is reached, it cannot be attributed to any single individual. Although extensive research has been conducted in swarm robotics using hand-coded design techniques to develop individual mechanisms for collective decision-making, the proposed mechanisms are generally limited in terms of robustness, scalability, and adaptability. In this paper, we employ evolutionary computation techniques to synthesise neural network-based decision-modules underpinning the individual opinion selection in robots. We describe the group dynamics underlying the decision process that leads to consensus in a three-option perceptual discrimination task. We test the robustness, scalability and adaptability of the decision-module in a variety of conditions. We show that the decision-making mechanisms underpinned by the evolved decision-module are more effective in supporting the collective decision-making process than the hand-coded voter and majority models, both in terms of accuracy and with respect to time to convergence to consensus.","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":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86035030","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: 1

Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression? 小批处理，梯度裁剪，一阶与二阶:符号回归中基于梯度的系数优化是什么?

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590368

Joe Harrison, M. Virgolin, T. Alderliesten, P. Bosman

{"title":"Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression?","authors":"Joe Harrison, M. Virgolin, T. Alderliesten, P. Bosman","doi":"10.1145/3583131.3590368","DOIUrl":"https://doi.org/10.1145/3583131.3590368","url":null,"abstract":"The aim of Symbolic Regression (SR) is to discover interpretable expressions that accurately describe data. The accuracy of an expression depends on both its structure and coefficients. To keep the structure simple enough to be interpretable, effective coefficient optimisation becomes key. Gradient-based optimisation is clearly effective at training neural networks in Deep Learning (DL), which can essentially be viewed as large, over-parameterised expressions: in this paper, we study how gradient-based optimisation techniques as often used in DL transfer to SR. In particular, we first assess what techniques work well across random SR expressions, independent of any specific SR algorithm. We find that mini-batching and gradient-clipping can be helpful (similar to DL), while second-order optimisers outperform first-order ones (different from DL). Next, we consider whether including gradient-based optimisation in Genetic Programming (GP), a classic SR algorithm, is beneficial. On five real-world datasets, in a generation-based comparison, we find that second-order optimisation outperforms coefficient mutation (or no optimisation). However, in time-based comparisons, performance gaps shrink substantially because the computational expensiveness of second-order optimisation causes GP to perform fewer generations. The interplay of computational costs between the optimisation of structure and coefficients is thus a critical aspect to consider.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"120 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80178147","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 and Efficient Local-Search for Genetic Programming Based Loss Function Learning 基于损失函数学习的遗传规划快速高效局部搜索

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590361

Christian Raymond, Qi Chen, Bing Xue, Mengjie Zhang

引用次数: 0

A Fully-distributed Shape-aware Neural Controller for Modular Robots 模块化机器人的全分布式形状感知神经控制器

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590419

Giorgia Nadizar, E. Medvet, Kathryn Walker, S. Risi

{"title":"A Fully-distributed Shape-aware Neural Controller for Modular Robots","authors":"Giorgia Nadizar, E. Medvet, Kathryn Walker, S. Risi","doi":"10.1145/3583131.3590419","DOIUrl":"https://doi.org/10.1145/3583131.3590419","url":null,"abstract":"Modular robots are promising for their versatility and large design freedom. Modularity can also enable automatic assembly and reconfiguration, be it autonomous or via external machinery. However, these procedures are error-prone and often result in misassemblings. This, in turn, can cause catastrophic effects on the robot functionality, as the controller deployed in each module is optimized for a different robot shape than the actual one. In this work, we address such shortcoming by proposing a shape-aware modular controller, operating with (1) a self-discovery phase, in which each module controller identifies the shape it is assembled in, followed by (2) a parameter selection phase, where the controller selects its parameters according to the inferred shape. We deploy a self-classifying neural cellular automaton for phase (1), and we leverage evolutionary optimization for implementing a library of controller parameters for phase (2). We test the validity of the proposed method considering voxel-based soft robots, a class of modular soft robots, and the task of locomotion. Our findings confirm the effectiveness of such a controller paradigm, and also show that it can be used to partially overcome unforeseen damages or assembly mistakes.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82412268","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

Effects of Objective Space Normalization in Multi-Objective Evolutionary Algorithms on Real-World Problems 多目标进化算法中目标空间归一化对现实问题的影响

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590450

Linjun He, Yang Nan, H. Ishibuchi, D. Srinivasan

引用次数: 0

Co-evolution improves the efficiency of preference learning methods when the Decision Maker's aspirations develop over time 当决策者的愿望随着时间的推移而发展时，共同进化提高了偏好学习方法的效率

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590348

Michał K. Tomczyk, M. Kadziński

{"title":"Co-evolution improves the efficiency of preference learning methods when the Decision Maker's aspirations develop over time","authors":"Michał K. Tomczyk, M. Kadziński","doi":"10.1145/3583131.3590348","DOIUrl":"https://doi.org/10.1145/3583131.3590348","url":null,"abstract":"This paper's research scope is interactive evolutionary multiple objective optimization founded on the preference learning paradigm. It concerns a scenario in which the Decision Maker's (DM's) aspirations develop over time. In this view, the interactive method may be forced to occasionally re-learn the DM's value system and, thus, re-orient the search during optimization. In preliminary studies, we observed that although a satisfactory recommendation can ultimately be discovered, it is often attainable with more significant computational power. To resolve this issue, we propose a co-evolutionary method, evolving sub-populations that approximate the Pareto front or align with the DM's preferences. This diversifies the maintained solution set, which is useful when re-understanding the DM's aspirations during interactions. Also, it helps reallocate the preference-driven sub-population quickly, avoiding an extensive computational burden. We demonstrate the usefulness of such hybridization in a series of extensive experiments that involve different test problems, numbers of objectives, and behavioral models of the Decision Maker.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89792118","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: 1

Grammar-guided Linear Genetic Programming for Dynamic Job Shop Scheduling 动态作业车间调度的语法引导线性遗传规划

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590394

Zhiyi Huang, Yi Mei, Fangfang Zhang, Mengjie Zhang

引用次数: 0

Combined Layout Optimization of Wind Farm and Cable Connection on Complex Terrain Using a Genetic Algorithm 基于遗传算法的复杂地形风电场与电缆连接组合布局优化

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590377

Daiki Kiribuchi, Ryoko Hatakeyama, Tomoshi Otsuki, Tatsuya Yoshioka, Kana Konno, Takumi Matsuda

{"title":"Combined Layout Optimization of Wind Farm and Cable Connection on Complex Terrain Using a Genetic Algorithm","authors":"Daiki Kiribuchi, Ryoko Hatakeyama, Tomoshi Otsuki, Tatsuya Yoshioka, Kana Konno, Takumi Matsuda","doi":"10.1145/3583131.3590377","DOIUrl":"https://doi.org/10.1145/3583131.3590377","url":null,"abstract":"To improve the economic efficiency of wind farms, this paper proposes a method for simultaneously optimizing wind farm layout and cabling on complex terrain such as mountainous areas, which most previous studies have not considered. Multiple wind turbines should be placed to maximize energy production while minimizing the cable length (between wind turbines and between the substation and wind turbines). To optimize both, especially on complex terrain where wind speeds at a site are not constant, the proposed method combines a genetic algorithm (NSGA-II) and a capacitated minimum spanning tree approximation algorithm (Esau-Williams algorithm). For five sites with complex terrain, the proposed method is compared with the exact optimal solution obtained by the weighted sum method using the integer linear programming formulation. For a small number of candidate locations, the proposed method obtains a hyper-volume equivalent to the exact solution. In comparison, the proposed method can obtain a larger hyper-volume even in the case of many candidate locations where the weighted sum method is computationally infeasible in terms of practical resources and time. These results indicate that the proposed method effectively contributes to the wind farm design on complex terrain.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74067933","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

Combining Evolutionary Algorithms with Reaction Rules Towards Focused Molecular Design 结合进化算法和反应规则的集中分子设计

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590413

João Correia, Vítor Pereira, M. Rocha

{"title":"Combining Evolutionary Algorithms with Reaction Rules Towards Focused Molecular Design","authors":"João Correia, Vítor Pereira, M. Rocha","doi":"10.1145/3583131.3590413","DOIUrl":"https://doi.org/10.1145/3583131.3590413","url":null,"abstract":"Designing novel small molecules with desirable properties and feasible synthesis continues to pose a significant challenge in drug discovery, particularly in the realm of natural products. Reaction-based gradient-free methods are promising approaches for designing new molecules as they ensure synthetic feasibility and provide potential synthesis paths. However, it is important to note that the novelty and diversity of the generated molecules highly depend on the availability of comprehensive reaction templates. To address this challenge, we introduce ReactEA, a new open-source evolutionary framework for computer-aided drug discovery that solely utilizes biochemical reaction rules. ReactEA optimizes molecular properties using a comprehensive set of 22,949 reaction rules, ensuring chemical validity and synthetic feasibility. ReactEA is versatile, as it can virtually optimize any objective function and track potential synthetic routes during the optimization process. To demonstrate its effectiveness, we apply ReactEA to various case studies, including the design of novel drug-like molecules and the optimization of pre-existing ligands. The results show that ReactEA consistently generates novel molecules with improved properties and reasonable synthetic routes, even for complex tasks such as improving binding affinity against the PARP1 enzyme when compared to existing inhibitors.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"127 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73048998","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