An initial study of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan generated wind conditions after being physically instantiated by a 3D printer has recently been presented. Unlike other approaches, such as computational fluid dynamics simulations , no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.
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Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single-or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.
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Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class.
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The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Abstract Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surro-gate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.
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Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally , uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.
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Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this article, we focus upon the coevolutionary nature of the design process when it is decomposed into concurrent sub-design-threads due to the overall complexity of the task. Using an abstract, tunable model of coevolution, we consider strategies to sample subthread designs for whole-system testing and how best to construct and use surrogate models within the coevolutionary scenario. Drawing on our findings, we then describe the effective design of an array of six heterogeneous vertical-axis wind turbines.
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Like most Evolutionary Algorithms (EAs), Particle Swarm Optimization (PSO) usually requires a large number of fitness evaluations to obtain a sufficiently good solution. This poses an obstacle for applying PSO to com-putationally expensive problems. This paper proposes a two-layer surrogate-assisted PSO (TLSAPSO) algorithm, in which a global and a number of local surrogate models are employed for fitness approximation. The global surrogate model aims to smooth out the local optima of the original multimodal fitness function and guide the swarm to fly quickly to an optimum. In the meantime, a local surrogate model constructed using the data samples near the particle is built to achieve a fitness estimation as accurate as possible. The contribution of each surrogate in the search is empirically verified by experiments on uni-and multi-modal problems. The performance of the proposed TLSAPSO algorithm is examined on ten widely used benchmark problems, and the experimental results show that the proposed algorithm is effective and highly competitive with the state-of-the-art, especially for multimodal optimization problems.
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基于代理的优化和自然启发的元启发式已成为解决实际优化问题的最先进技术。尽管如此,对于初学者甚至专家来说,难以获得一个概述来解释与优化范围内的大量可用方法相比的优势。可用的分类法缺乏基于学习方法的整合,因此它们嵌入在这个广阔领域的更大范围内。本文介绍了该领域的分类,它进一步匹配了自然启发算法的思想,因为它基于路径发现中的人类行为。直观的类比使得很容易设想搜索算法的最基本原则,即使对于这个研究领域的初学者和非专家也是如此。然而,该方案并未过度简化不同算法的高复杂度,因为类标识符仅定义算法搜索策略的描述性元级别。通过探索和匹配算法方案,提取相似性和差异,以及创建一组分类指标来区分五个不同的类,建立了分类法。在实践中,这种分类允许建议相应算法的适用性,并帮助开发人员尝试创建或改进他们自己的算法。
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设计优化技术通常在设计过程的开始使用,以探索可能的设计空间。在这些领域中,诸如MAP-Elites之类的光谱算法是经典优化算法的有前途的替代方案,因为它们在单次运行中产生多样化的高质量解决方案,而不是仅仅是单个近似最优解决方案。不幸的是,这些算法目前需要大量的功能评估,限制了它们的适用性。在本文中,我们介绍了一种新的照明算法,代理辅助照明(SAIL),它利用代理模型技术根据头像定义的特征创建设计空间的地图,同时最小化适应度评估的数量。在二维翼型优化问题上,SAIL产生数百种不同但高性能的设计,其评估比MAP-Elites或CMA-ES少几个数量级。我们证明SAIL还能够在真实的三维叶轮动力学任务中生成高性能设计图,并具有精确的流动模拟。使用SAIL进行数据有效的设计探索可以帮助设计人员通过考虑纯粹的基于客观的优化来理解什么是可能的,而不是最优的。
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Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to real-world applications is that EAs usually need a large number of fitness evaluations before a satisfying result can be obtained. However, fitness evaluations are not always straightforward in many real-world applications. Either an explicit fitness function does not exist, or the evaluation of the fitness is computationally very expensive. In both cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a comprehensive survey of the research on fitness approximation in evolutionary computation is presented. Main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed. To conclude, open questions and interesting issues in the field are discussed.
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Many multi-objective optimisation problems incorporate computa-tionally or nancially expensive objective functions. State-of-the-art algorithms therefore construct surrogate model(s) of the parameter space to objective functions mapping to guide the choice of the next solution to expensively evaluate. Starting from an initial set of solutions, an innll criterion-a surrogate-based indicator of quality-is extremised to determine which solution to evaluate next, until the budget of expensive evaluations is exhausted. Many successful innll criteria are dependent on multi-dimensional integration, which may result in innll criteria that are themselves impractically expensive. We propose a computationally cheap innll criterion based on the minimum probability of improvement over the estimated Pareto set. We also present a range of set-based scalarisation methods modelling hypervolume contribution, dominance ratio and distance measures. ese permit the use of straightforward expected improvement as a cheap innll criterion. We investigated the performance of these novel strategies on standard multi-objective test problems, and compared them with the popular SMS-EGO and ParEGO methods. Unsurprisingly, our experiments show that the best strategy is problem dependent, but in many cases a cheaper strategy is at least as good as more expensive alternatives.
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Using surrogate models in evolutionary search provides an efficient means of handling today's complex applications plagued with increasing high-computational needs. Recent surrogate-assisted evolutionary frameworks have relied on the use of a variety of different modeling approaches to approximate the complex problem landscape. From these recent studies, one main research issue is with the choice of modeling scheme used, which has been found to affect the performance of evolutionary search significantly. Given that theoretical knowledge available for making a decision on an approximation model a priori is very much limited, this paper describes a generalization of surrogate-assisted evolutionary frameworks for optimization of problems with objectives and constraints that are computationally expensive to evaluate. The generalized evolutionary framework unifies diverse surrogate models synergistically in the evolutionary search. In particular, it focuses on attaining reliable search performance in the surrogate-assisted evolutionary framework by working on two major issues: 1) to mitigate the 'curse of uncertainty' robustly, and 2) to benefit from the 'bless of uncertainty.' The backbone of the generalized framework is a surrogate-assisted memetic algorithm that conducts simultaneous local searches using ensemble and smoothing surrogate models, with the aims of generating reliable fitness prediction and search improvements simultaneously. Empirical study on commonly used optimization benchmark problems indicates that the generalized framework is capable of attaining reliable, high quality, and efficient performance under a limited computational budget. Index Terms-Approximation models, computationally expensive problems, memetic algorithms, metamodels, surrogate models , surrogate-assisted evolutionary algorithms.
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Traditional evolutionary computing techniques use an explicit fitness function-mathematical or simulated-to derive a solution to a problem from a population of individuals, over a number of generations. In this paper an architecture is presented which allows such techniques to be used on problems which cannot be expressed mathematically or which are difficult to simulate. A neural network is trained using example individuals with their explicit fitness and the resulting model of the fitness function is then used by the evolutionary algorithm to find a solution. It is shown that the approach is effective over a wide range of function types in comparison to the traditional approach. Finally its application to a user-agent task is described-a system in which the fitness function is purely subjective.
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Many complex control problems require sophisticated solutions that are not amenable to traditional controller design. Not only is it difficult to model real world systems, but often it is unclear what kind of behavior is required to solve the task. Reinforcement learning (RL) approaches have made progress by using direct interaction with the task environment, but have so far not scaled well to large state spaces and environments that are not fully observable. In recent years, neuroevolution, the artificial evolution of neural networks, has had remarkable success in tasks that exhibit these two properties. In this paper, we compare a neuroevolution method called Cooperative Synapse Neuroevolution (CoSyNE), that uses cooperative coevolution at the level of individual synaptic weights, to a broad range of reinforcement learning algorithms on very difficult versions of the pole balancing problem that involve large (continuous) state spaces and hidden state. CoSyNE is shown to be significantly more efficient and powerful than the other methods on these tasks.
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More than a decade after the first extensive overview on parameter control, we revisit the field and present a survey of the state of the art. We briefly summarise the development of the field and discuss existing work related to each major parameter or component of an evolutionary algorithm. Based on this overview we observe trends in the area, identify some (methodological) shortcomings, and give recommendations for future research.
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Traditional evolutionary computing techniques use an explicit fitness function-mathematical or simulated-to derive a solution to a problem from a population of individuals , over a number of generations. In this paper an approach which allows such techniques to be used on problems in which evaluations are costly, which cannot be expressed formally, or which are difficult to simulate, is examined. A neural network is trained using example individuals with the explicit fitness and the resulting model of the fitness function is then used by the evolutionary algorithm to find a solution. It is shown that the approach is effective over a small range of function types in comparison to the traditional approach when limited training data is available. An iterative step is then added whereby after a number of generations the current best individual in a population is evaluated directly on the explicit fitness function. The individual and its ''real'' fitness are then added to the training data and the neural network is retrained to improve its approximation of the fitness function. It is shown that in this way the performance of the model-based architecture is greatly improved on more rugged/complex landscapes without a large increase in the amount of training data required.
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[1] Surrogate modeling, also called metamodeling, has evolved and been extensively used over the past decades. A wide variety of methods and tools have been introduced for surrogate modeling aiming to develop and utilize computationally more efficient surrogates of high-fidelity models mostly in optimization frameworks. This paper reviews, analyzes, and categorizes research efforts on surrogate modeling and applications with an emphasis on the research accomplished in the water resources field. The review analyzes 48 references on surrogate modeling arising from water resources and also screens out more than 100 references from the broader research community. Two broad families of surrogates namely response surface surrogates, which are statistical or empirical data-driven models emulating the high-fidelity model responses, and lower-fidelity physically based surrogates, which are simplified models of the original system, are detailed in this paper. Taxonomies on surrogate modeling frameworks, practical details, advances, challenges, and limitations are outlined. Important observations and some guidance for surrogate modeling decisions are provided along with a list of important future research directions that would benefit the common sampling and search (optimization) analyses found in water resources. Citation: Razavi, S., B. A. Tolson, and D. H. Burn (2012), Review of surrogate modeling in water resources, Water Resour. Res., 48, W07401,
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近似贝叶斯计算(ABC)是贝叶斯推理的一种方法,当可能性不可用时,但是可以从模型中进行模拟。然而,许多ABC算法需要大量的模拟,这可能是昂贵的。为了降低计算成本,已经提出了贝叶斯优化(BO)和诸如高斯过程的模拟模型。贝叶斯优化使人们可以智能地决定在哪里评估模型下一个,但是常见的BO策略不是为了估计后验分布而设计的。我们的论文解决了文献中的这一差距。我们建议计算ABC后验密度的不确定性,这是因为缺乏模拟来准确估计这个数量,并且定义了测量这种不确定性的aloss函数。然后,我们建议选择下一个评估位置,以尽量减少预期的损失。实验表明,与普通BO策略相比,所提出的方法通常产生最准确的近似。
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A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.
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在实际系统中非常昂贵的问题是非常普遍的,因为一个适合的评估花费几个小时甚至几天。在过去的几十年中,替代辅助进化算法(SAEAs)已被广泛用于解决这一关键问题。然而,大多数研究的SAEA专注于解决问题,至少十倍的问题维度,这在许多非常昂贵的现实问题中是不可接受的。在本文中,我们使用Voronoidiagram来提高SAEA的性能,并提出一个新的框架,名为基于Voronoi的有效代理协助进化算法(VESAEA),用于解决非常昂贵的问题,其中优化预算在fitnessevaluations方面仅为5倍。问题的维度。在提议的框架中,Voronoi图将整个搜索空间划分为若几个子空间,然后本地搜索在一些可能的更好的子空间中运行。此外,为了权衡勘探和开发,该框架涉及通过组合一次性交叉验证和径向基函数替代模型而开发的全局搜索阶段。性能选择器旨在在全局和本地搜索阶段之间动态和自动切换搜索。各种基准问题的实证结果表明,所提出的框架显着优于具有极其有限的适应性评估的几种最先进的算法。此外,还进一步分析了Voronoi图的功效,结果表明它有可能优化非常昂贵的问题。
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