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

{"title":"Effects of Dominance Modification on Hypervolume-based and IGD-based Performance Evaluation Results of NSGA-II","authors":"H. Ishibuchi, Lie Meng Pang, Ke Shang","doi":"10.1145/3583131.3590437","DOIUrl":"https://doi.org/10.1145/3583131.3590437","url":null,"abstract":"In the field of evolutionary multi-objective optimization, it is well known that dominance-based algorithms do not work well on many-objective problems. This is because almost all solutions in a population become non-dominated in early generations. Two approaches have been proposed to decrease the number of non-dominated solutions. One is to increase the dominated region by each solution: dominance modification. The other is to increase the correlation among objectives: objective modification. In this paper, first we show that these two approaches can be viewed as the same approach. We also explain that some regions of the Pareto front are dominated when the dominated region is increased. Next, we numerically examine the effects of dominance modification on the performance of NSGA-II on many-objective test problems. Through computational experiments, we demonstrate that its positive and negative effects are clearly shown by the hypervolume (HV) and inverted generational distance (IGD) indicators, respectively. Then, we discuss why these two indicators emphasize different effects of dominance modification using the optimal distribution of solutions for each indicator. Finally, we explain that objective space normalization is needed in dominance modification whereas it has no effects on the Pareto dominance relation.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84956646","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}

{"title":"Factored Particle Swarm Optimization for Policy Co-training in Reinforcement Learning","authors":"Kordel K. France, John W. Sheppard","doi":"10.1145/3583131.3590376","DOIUrl":"https://doi.org/10.1145/3583131.3590376","url":null,"abstract":"Uncertainty of the environment limits the circumstances with which any optimization problem can provide meaningful information. Multiple optimizers can combat this problem by communicating different information through cooperative coevolution. In reinforcement learning (RL), uncertainty can be reduced by applying learned policies collaboratively with another agent. Here, we propose policy Co-training with Factored Evolutionary Algorithms (CoFEA) to evolve an optimal policy for such scenarios. We hypothesize that self-paced co-training can allow factored particle swarms with imperfect knowledge to consolidate knowledge from each of their imperfect policies in order to approximate a single optimal policy. Additionally, we show how the performance of co-training swarms of RL agents can be maximized through the specific use of Expected SARSA as the policy learner. We evaluate CoFEA against comparable RL algorithms and attempt to establish limits for which our procedure does and does not provide benefit. Our results indicate that Particle Swarm Optimization (PSO) is effective in training multiple agents under uncertainty and that FEA reduces swarm and policy updates. This paper contributes to the field of cooperative co-evolutionary algorithms by proposing a method by which factored evolutionary techniques can significantly improve how multiple RL agents collaborate under extreme uncertainty to solve complex tasks faster than a single agent can under identical conditions.","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":"77592600","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}

{"title":"First Improvement Hill Climber with Linkage Learning -- on Introducing Dark Gray-Box Optimization into Statistical Linkage Learning Genetic Algorithms","authors":"M. Przewozniczek, R. Tinós, M. Komarnicki","doi":"10.1145/3583131.3590495","DOIUrl":"https://doi.org/10.1145/3583131.3590495","url":null,"abstract":"Gray-box optimization requires user-supported information about inter-variable dependencies to propose more effective optimizers for hard combinatorial problems. In Black-box optimization, such information is unavailable. Therefore, the Gray-box operators are only usable in Black-box scenarios if an optimizer can discover the inter-variable dependencies independently. Empirical Linkage Learning (ELL) techniques are guaranteed to discover only the true dependencies, which led to proposing the Dark Gray-box optimizers class. Such optimizers use ELL to construct Empirical Variable Interaction Graph (eVIG), which may miss some dependencies but contains only the true ones. eVIG allows using Gray-box operators in Black-box scenarios. ELL techniques are computationally expensive. Therefore, the recently proposed Local Search with Linkage Learning (LSwLL) is promising because it makes ELL a no-cost technique. However, LSwLL has some disadvantages. First, it can decompose only the problems of additive nature. Second, LSwLL removes ELL costs, but in some optimization scenarios, it may be expensive itself. Therefore, we propose the First Improvement Hill Climber with Linkage Learning (FIHCwLL). FIHCwLL decomposes additive and non-additive problems, and its overall costs are frequently lower than LSwLL (although ELL is not no-cost anymore). We introduce FIHCwLL into two state-of-the-art model-building optimizers, creating two new Dark Gray-box optimizers of significantly improved effectiveness.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"16 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80089706","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}

{"title":"Adaptive Donor Selection Mixing for Multi-objective Optimization: an Enhanced Variant of MO-GOMEA","authors":"H. Liao, Wentai Fang, Tian-Li Yu","doi":"10.1145/3583131.3590403","DOIUrl":"https://doi.org/10.1145/3583131.3590403","url":null,"abstract":"The multi-objective gene-pool optimal mixing evolutionary algorithm with interleaved multi-start scheme (MO-GOMEA) is a powerful, parameterless model-based genetic algorithm that excels at solving multi-objective combinatorial optimization problems. In this paper, we propose a new mixing mechanism, adaptive donor selection mixing (ADSM) and further integrate it into MO-GOMEA to form a new variant, ADSM-MO-GOMEA. The proposed ADSM mechanism adaptively switches between cluster-guided and elitist-guided mixing, with the latter having a customized donor selection for the receiver based on empirical observations and mathematical derivation. The empirical results on multiple benchmark problems indicate that ADSM-MO-GOMEA improves the effectiveness over the original MO-GOMEA and achieves a lower inverted generational diversity and higher front occupation within the given limited number of evaluations.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"4 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78329625","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}

{"title":"To Combine or not to Combine Graybox Crossover and Local Search?","authors":"Lorenzo Canonne, B. Derbel, F. Chicano, G. Ochoa","doi":"10.1145/3583131.3590466","DOIUrl":"https://doi.org/10.1145/3583131.3590466","url":null,"abstract":"Specialized graybox local search and crossover have been successfully combined within the framework of the so-called Drils (Deterministic recombination and iterated local search) algorithm. As for any evolutionary algorithm, the initial design framework, and the underlying high-level choices and parameters, are crucially important. The Drils algorithm is no exception, and recent enhanced variants exist in the literature. In this paper, we aim at: (i) improving the performance of the latest variants of Drils, and (ii) providing a better principled understanding of graybox search behavior and dynamics. On the basis of a preliminary analysis using Local Optima Networks of small-size NKQ-landscapes, we first highlight the difference of using local search with and without crossover. We then propose to pipeline these two techniques in a simple two-phase like iterated local search scheme which is shown to provide substantial improvements over the latest Drils+ variant for large-size NKQ-landscapes. We further report a dedicated analysis in an attempt to provide new insights into the impact of local search and crossover on the phenotype and the genotype of the local optima encountered in the search trajectory.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"89 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90976705","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}

{"title":"Super-Human and Super-AI Cognitive Augmentation of Human and Human-AI Teams Assisted by Brain Computer Interfaces","authors":"R. Poli","doi":"10.1145/3583131.3603554","DOIUrl":"https://doi.org/10.1145/3583131.3603554","url":null,"abstract":"In the last 10 years a major strand of my research within the Essex Brain-Computer Interfaces and Neural Engineering (BCI-NE) laboratory has focused on the idea of combining brain signals (and other physiological and behavioral information) across multiple people to achieve a form of emergent, perceptual and, more generally cognitive, group augmentation, which is more than the sum of the parts. Over this period (in projects funded mostly by the UK Ministry of Defense and also US DOD) we have developed a technology which has delivered significant (and in fact in some cases remarkable) improvements over the group performance achieved by more traditional methods of integrating information applying it successfully to progressively more and more real-world applications. Our efforts have particularly focused on the area of decision making. Decisions (for example made by government, military or hospital management) are often made with limited amounts of information, or indeed too much information for any single person to take in, hence involving a high degree of uncertainty. Yet, such decisions can be highly critical in nature, with mistakes possibly resulting in extremely adverse outcomes, including loss of lives. So, any improvements in accuracy or speed of decisions in such conditions is vitally important. In the last 5 years we have also started to study hybrid human-AI decision-making groups by the inclusion of one or more AI-based teammates which act as peers to the humans. We found that when the conditions are right (more on this in the talk), human-AI groups produce super-human and super-AI performance. In this presentation, I will review BCIs and our approach, and will discuss some of the applications we explored including the identification of visual targets in cluttered environments, the comprehension of military radio communication, face recognition, military recognisance missions, military outposts and strategic resource allocation in a pandemic. I will finally look at potential future developments.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"214 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86850049","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}

{"title":"Multi-Objective Seed Curve Optimization for Coverage Path Planning in Precision Farming","authors":"Lukas Bostelmann-Arp, Christoph Steup, Sanaz Mostaghim","doi":"10.1145/3583131.3590490","DOIUrl":"https://doi.org/10.1145/3583131.3590490","url":null,"abstract":"Coverage path planning is one of the main challenges in precision farming. Here the goal is to compute a continuous optimal working path by combining individual tracks such that the entire field can be cultivated in the most efficient way. Various 2D and 3D approaches have already been presented in the literature. However, these are lacking in two major aspects: In each case, a seed curve is determined and offsetted to generate the individual tracks, but the seed curve is usually selected from a predefined set of alternatives. Additionally, the utilized objectives do not consider the efficiency of the tools used to work on the field, which typically depends on the path angle and the field's inclination. In this paper, we propose a scheme to cope with both aspects through an evolutionary multi-objective optimization approach. Three objectives are used, namely coverage, energy based on anisotropic friction and gravity, and precession, which describes the angle between the path direction and the gradient of the terrain.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89220706","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}

{"title":"Evolutionary Approach to Recommender Systems Improvement by Directory of Products Optimization","authors":"Piotr Lipiński","doi":"10.1145/3583131.3590519","DOIUrl":"https://doi.org/10.1145/3583131.3590519","url":null,"abstract":"This paper concerns improving recommender systems using evolutionary algorithms that optimize the composition of the directory of products in the recommender system. It focuses on merging some infrequent products into certain groups of similar products and replacing the regular products by some aggregates of products, such as a category of products or a cluster of products in the product embedding space, and preparing recommendations with such aggregates. Evolutionary algorithms endeavor to decide which products should be processed as single products and which ones should be merged with others into smaller or larger groups of products (merging products into groups is done by a given auxiliary merging strategy that cluster similar products into groups) with the aim to maximize the accuracy of the recommender system. Computational experiments on benchmark datasets confirmed the efficiency of the approach.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80985342","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}

{"title":"Decomposition-Based Multi-Objective Evolutionary Algorithm with Model-Based Ideal Point Estimation","authors":"Yin Wu, Ruihao Zheng, Zhenkun Wang","doi":"10.1145/3583131.3590521","DOIUrl":"https://doi.org/10.1145/3583131.3590521","url":null,"abstract":"The ideal point is critical in the multi-objective optimization problem (MOP), which consists of the best value of each objective. It is widely used for normalizing the objective space and guiding the evolution of the population. Since the ideal point cannot know prior, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) takes the best objective values of the population as the estimated ideal point. However, the population-based ideal point estimation may cause the estimated ideal point to be appropriate for (1) no objective or (2) only some objectives. In our analysis, the unreliable estimation deteriorates the performance of MOEA/D. These two scenarios often occur when the MOP with mixed bias (i.e., position-related bias and distance-related bias). To overcome this, we propose to incorporate the model-based ideal point estimation in MOEA/D. The new algorithm (called MOEA/D-MIPE) employs the radial basis function model and a remedy scheme to estimate the ideal point. In experimental studies, we compare MOEA/D-MIPE with seven state-of-the-art algorithms on various MOPs. The results show that MOEA/D-MIPE has excellent potential.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"37 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75872422","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}

{"title":"Effects of Including Optimal Solutions into Initial Population on Evolutionary Multiobjective Optimization","authors":"Cheng Gong, Yang Nan, Lie Meng Pang, Qingfu Zhang, H. Ishibuchi","doi":"10.1145/3583131.3590515","DOIUrl":"https://doi.org/10.1145/3583131.3590515","url":null,"abstract":"A long-standing question in the evolutionary multi-objective (EMO) community is how to generate a good initial population for EMO algorithms. Intuitively, as the starting point of optimization, a good initial population can have positive effects on the performance of EMO algorithms. However, in most existing EMO algorithms, one of the commonly-used initialization methods is to randomly generate a set of solutions as an initial population. One possible approach to improve random initialization is to include one or more Pareto optimal (near Pareto optimal) solution(s) in the initial population, which are expected to provide useful information and knowledge on the optimized problem. In this paper, to investigate the effectiveness of this initialization idea, we examine and quantify the effects of including one or more Pareto optimal solution(s) in the initial population on the performance of EMO algorithms. Experimental results demonstrate that it is worthwhile to first obtain and then include some Pareto optimal solutions in the initial population. Through a number of experiments and algorithm behavior analysis, this study provides supports and insights into EMO algorithm design and motivates further research on population initialization for EMO algorithms.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76534545","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}