Theoretical studies on evolutionary algorithms have developed vigorously in recent years. Many such algorithms have theoretical guarantees in both running time and approximation ratio. Some approximation mechanism see...
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In this work, we employed ab initio methods combined with evolutionary algorithms for searching stable structures for fluorine in the terapascal (TPa) regime. We performed several structural searches using the USPEX c...
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Optimizing gait stability for legged robots is a difficult problem. Even on level surfaces, effectively traversing across different textures (e.g., carpet) rests on dynamically tuning parameters in multidimensional sp...
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One of the main problems of evolutionary algorithms is the convergence of the population to local minima. In this paper, we explore techniques that can avoid this problem by encouraging a diverse behavior of the agent...
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While the theoretical analysis of evolutionary algorithms (EAs) has made significant progress for pseudo-Boolean optimization problems in the last 25 years, only sporadic theoretical results exist on how EAs solve per...
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Interactive dynamic influence diagrams~(I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into...
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ISBN:
(纸本)9781450392136
Interactive dynamic influence diagrams~(I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms.
Multi-Objective evolutionary algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms...
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Multi-Objective evolutionary algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed.
Scramjet engines are a hypersonic airbreathing technology that offers a potential for economical and flexible space transportation in lieu of traditional rocket-based systems. Accurate prediction of inviscid flowfield...
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Scramjet engines are a hypersonic airbreathing technology that offers a potential for economical and flexible space transportation in lieu of traditional rocket-based systems. Accurate prediction of inviscid flowfields is of particular importance for high-performance intake design, prior to consideration of viscous effects in the design process. Further, inviscid axisymmetric intakes serve as a base for streamline tracing, one of the most promising design methodologies for scramjet intakes. Multi-objective optimization studies have been conducted via surrogate-assisted evolutionary algorithm to gain physical insights into axisymmetric intake design in this study. The results indicate the existence of global optimum solutions that can simultaneously achieve maximum compression efficiency and minimum drag for any degree of compression in case the outflow is supersonic at the intake exit, which has been verified by theory. In addition, a correlation between compression efficiency and flow uniformity has been found and discussed quantitatively. This assures the optimality of the Busemann intakes in that they simultaneously offer high compression efficiency and uniform flow at the intake exit in the inviscid regime. (C) 2021 Elsevier Masson SAS. All rights reserved.
The population initialization step is a common step in the majority (or even all) of evolutionary algorithms (EAs). There are many population initialization techniques. Due to the limited population size and the high ...
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The population initialization step is a common step in the majority (or even all) of evolutionary algorithms (EAs). There are many population initialization techniques. Due to the limited population size and the high dimensionality of many problems, there is little chance to cover the promising regions in the search space. From different perspectives, this paper compares the stochastic and deterministic population initialization techniques through comparing five of the well-known population initializers: Random number generator (RNG), Latin Hypercube, Sobol, Halton, and Kronecker. Due to the presence of many constraints in real-world applications, in this paper, we are focusing only on single-objective constrained optimization problems. Specifically, the goal is to investigate if there is a significant difference between these population initialization methods. In this paper, we explain theoretically and mathematically these different population initialization techniques. Moreover, different illustrative examples and visualizations are introduced to explain the behavior of each technique and compare different techniques from different perspectives. The results show that due to the high uniformity of the low-discrepancy sequences such as the Halton and Sobol sequences, the generated points using these sequences are more evenly distributed over the space than RNG, which is the commonly used technique for initializing the populations in EAs. Practically, using a set of benchmark functions, we investigate the use of each population initialization technique for initializing different population-based evolutionary algorithms. The results of our experiments prove that with sufficient numbers of iterations, the EAs are not sensitive to the initialization methods and there are no significant differences between the mentioned population initialization methods. Further, the low discrepancy methods enhance the exploration ability of EAs in early iterations.
In recent years carbon fibre reinforced plastics (CFRP) have gained enormous popularity in aircraft applications. Since the material is very expensive, costs have to be saved through an automated production. For the m...
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In recent years carbon fibre reinforced plastics (CFRP) have gained enormous popularity in aircraft applications. Since the material is very expensive, costs have to be saved through an automated production. For the manufacturing of large structures it is often advisable to use cooperating robots. However, a major problem for the economic use of complex components is the programming of the robot paths. Manual teach-in is no feasible solution and therefore often decides if automated production is profitable. In this work, a system is presented which automatically calculates robot paths using evolutionary algorithms. The use of the proposed system allows, to reduce the commissioning time drastically and changes to the process can be made without great effort by changing the component data.
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