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Evaluation of a Multi-agent "Human-in-the-loop" Game Design System

ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS 2022年第3期12卷 1–26页

作者： Kruse, Jan Connor, Andy M. Marks, Stefan Mediadesign Univ Appl Sci Franklinstr 28-29 D-10587 Berlin Germany Auckland Univ Technol AUT Private Bag 92006 Auckland 1142 New Zealand

Designing games is a complicated and time-consuming process, where developing new levels for existing games can take weeks. Procedural content generation offers the potential to shorten this timeframe, however, automated design tools are not adopted widely in the game industry. This article presents an expert evaluation of a human-in-the-loop generative design approach for commercial game maps that incorporates multiple computational agents. The evaluation aims to gauge the extent to which such an approach could support and be accepted by human game designers and to determine whether the computational agents improve the overall design. To evaluate the approach, 11 game designers utilized the approach to design game levels with the computational agents both active and inactive. Eye-tracking, observational, and think-aloud data was collected to determine whether designers favored levels suggested by the computational agents. This data was triangulated with qualitative data from semi-structured interviews that were used to gather overall opinions of the approach. The eye-tracking data indicates that the participating game level designers showed a clear preference for levels suggested by the computational agents, however, expert designers in particular appeared to reject the idea that the computational agents are helpful. The perception of computational tools not being useful needs to be addressed if procedural content generation approaches are to fulfill their potential for the game industry.

关键词： Human-based computation autonomous agents evolutionary computation multi-agent systems procedural content generation user evaluation

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Fixed-Target Runtime Analysis

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ALGORITHMICA 2022年第6期84卷 1762-1793页

作者： Buzdalov, Maxim Doerr, Benjamin Doerr, Carola Vinokurov, Dmitry ITMO Univ St Petersburg Russia Inst Polytech Paris Lab Informat LIX Ecole Polytech CNRS Palaiseau France Sorbonne Univ LIP6 CNRS Paris France

Runtime analysis aims at contributing to our understanding of evolutionary algorithms through mathematical analyses of their runtimes. In the context of discrete optimization problems, runtime analysis classically studies the time needed to find an optimal solution. However, both from a practical and from a theoretical viewpoint, more fine-grained performance measures are needed to gain a more detailed understanding of the main working principles and their resulting performance implications. Two complementary approaches have been suggested: fixed-budget analyses and fixed-target analyses. In this work, we conduct an in-depth study on the advantages and the limitations of fixed-target analyses. We show that, different from fixed-budget analyses, many classical methods from the runtime analysis of discrete evolutionary algorithms yield fixed-target results without greater effort. We use this to conduct a number of new fixed-target analyses. However, we also point out examples where an extension of existing runtime results to fixed-target results is highly non-trivial.

关键词： Runtime analysis evolutionary computation Randomized search heuristics

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Towards a parameterless out-of-the-box population size control for evolutionary and swarm-based algorithms for single objective bound constrained real-parameter numerical optimization

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APPLIED SOFT COMPUTING 2022年 123卷

作者： Pereira de Lacerda, Marcelo Gomes Amorim Neto, Hugo de Andrade Ludermir, Teresa Bernarda Kuchen, Herbert de Lima Neto, Fernando Buarque Univ Fed Pernambuco Recife PE Brazil Univ Pernambuco Recife PE Brazil Westfalische Wilhelms Univ Munster Munster Germany

We present an innovative step towards a parameterless out-of-the-box population size control for evolutionary and swarm-based algorithms for single objective bound constrained real-parameter numerical optimization. To the best of our knowledge, our approach is the first parameterless out-of-the-box parameter control for such a kind of technique. It is easy to implement and to use, since it does not require the adjustment of any parameter. The general idea is to increment the velocity of the population change if the best fitness stagnates, and decrement it otherwise. Then, in order to effectively change the population size, a mechanism of removal/addition of individuals inspired by the selection methods of evolutionary algorithms is executed. Our experimental results provide evidence that our controller is not only compatible with any evolutionary or swarm-based algorithm for single objective bound constrained real-parameter numerical optimization, but that it also performs well in many scenarios. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Population size control Parameter control Swarm intelligence evolutionary computation

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An adaptive hydrologic cycle optimization algorithm for numerical optimization and data clustering

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第9期37卷 6123-6151页

作者： Yan, Xiaohui Niu, Ben Chai, Yujuan Zhang, Zhicong Zhang, Liangwei Dongguan Univ Technol Sch Mech Engn Dongguan Peoples R China Shenzhen Univ Coll Management Shenzhen Peoples R China Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Shenzhen 518060 Peoples R China

The circulation and convergence of water in the hydrologic cycle process inspired us to design a new optimization algorithm, the Hydrologic Cycle Optimization (HCO) algorithm. In this study, a comprehensive demonstration of the HCO was presented. First, a simplified model of the hydrological cycle phenomenon was established. Then, the framework of HCO and its operators were discussed and verified in detail. Several experiments were done to test the optimization ability of the HCO algorithm. In the first experiment, the parameter settings were tested, and an adaptive version of the algorithm was proposed. Then the HCO was tested on 20 numeric optimization benchmark functions and eight data clustering sets, respectively, and compared with other algorithms. The experimental results showed that the HCO is superior to the compared algorithms, indicating that it is a competitive approach for numerical and engineering optimization problems.

关键词： computational intelligence evolutionary computation hydrologic cycle optimization optimization algorithm swarm intelligence

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A Diversity-Enhanced Subset Selection Framework for Multimodal Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2022年第5期26卷 886-900页

作者： Peng, Yiming Ishibuchi, Hisao Southern Univ Sci & Technol Guangdong Prov Key Lab Brain Inspired Intelligent Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

Multimodality is commonly seen in real-world multiobjective optimization problems (MOPs). In such optimization problems, namely, multimodal MOPs (MMOPs), multiple decision vectors can be projected to the same solution in the objective space (i.e., there are multiple implementations corresponding to that solution). Therefore, the diversity in the decision space is very important for the decision maker when tackling MMOPs. Subset selection methods have been widely used in the field of evolutionary multiobjective optimization for selecting well-distributed solutions (in the objective space) to be presented to the decision maker. However, since most subset selection methods do not consider the diversity of solutions in the decision space, they are not suitable for MMOPs. In this article, we aim to clearly demonstrate the usefulness of subset selection for multimodal multiobjective optimization. We propose a novel subset selection framework that can be easily integrated into existing multimodal multiobjective optimization algorithms. By selecting a prespecified number of solutions with good diversity in both the objective and decision spaces from all the examined solutions, the proposed framework significantly improves the performance of state-of-the-art multimodal multiobjective optimization algorithms on various test problems.

关键词： Optimization evolutionary computation Search problems Standards Spatial diversity Maintenance engineering Linear programming Decision space diversity multimodal multiobjective optimization objective space diversity subset selection

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Optimal design of a short primary double-sided linear induction motor based on derived Quasi-3D equivalent circuit model using an improved differential evolution

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IET ELECTRIC POWER APPLICATIONS 2022年第12期16卷 1521-1541页

作者： Wang, Hanming Zhao, Jinghong Xue, Jie Chen, Hansi Guo, Guoqiang Xiong, Yiyong Yan, Sinian Naval Univ Engn Sch Elect Engn Jiefang Rd 717 Wuhan Peoples R China

Due to its simple construction, the linear induction motor (LIM) provides a linear driving force without any intermediate motion translation system. LIMs are widely used in various industrial applications, including maglev rail transit and the national defense industry. However, LIMs are affected by the end effect and suffer from problems such as low efficiencies and low power factors. To make improvements, in this paper, an ensemble multi-objective optimal design method for a short primary double-sided linear induction motor (SP-DLIM) is proposed. First, a simplified Quasi-3D equivalent circuit model (ECM) for an SP-DLIM applicable to the model in this paper is derived. The 3-D transient finite element method and an experimental prototype are utilised to prove that the derived ECM is accurate enough to solve the SP-DLIM optimisation problem. Second, an ensemble multi-objective optimal design method of SP-DLIM is presented, with proposed design constraints and four different optimisation problems. Then, an improved differential evolutionary (IDE) algorithm is proposed to optimise the efficiency, power factor, and tooth weight of the motor. The three-dimensional time-stepping finite element method is utilised to verify the validity of the optimisation method. Further, a comparison of the results suggests that the IDE yields the best performance to those of other advanced heuristic algorithms.

关键词： equivalent circuits optimisation method linear driving force LIM derived Quasi3D equivalent circuit model improved differential evolutionary algorithm derived ECM multiobjective optimal design method improved differential evolution finite element analysis optimisation SP-DLIM optimisation problem double-sided linear induction motor simplified Quasi3D equivalent circuit model evolutionary computation power factor industrial applications low efficiencies linear induction motors different optimisation problems simple construction proposed design constraints maglev rail transit 3-D transient finite element method intermediate motion translation system low power factors national defense industry

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Time-Domain Electromagnetic Identification Based on Rectangular Grooves

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IEEE ACCESS 2022年 10卷 100104-100112页

作者： Kadlec, Petr Brno Univ Technol Fac Elect Engn & Commun Dept Radio Elect Lerch Lab EM Res Brno 61600 Czech Republic

Unique identification of goods and products is an integral part of today's automated world. Conventional optical systems cannot be operated in environments of poor visibility. The radio-frequency identification systems require expensive development, production, and installation of passive tags. In this paper, we propose a system that stores the information necessary for the distinction of individual items into a set of grooves with different geometric properties. The individual items are then identified based on observing the response to the electromagnetic wave in the time domain. The proposed identification system benefits from the cooperation of a semi-analytical computational scheme based on the Cagniard-DeHoop Method of Moments and a global optimization algorithm that solves the inverse problem of grooves characterization. The proposed system is validated on several computational examples. Also, the resilience of the proposed system to the influence of the noise added to the observed response is investigated. Finally, the influence of reflected signals on the accuracy of the system is assessed.

关键词： Radiofrequency identification Optimization Inverse problems Optical surface waves Time-domain analysis Surface waves Voltage control Radiofrequency identification inverse method particle swarm optimization evolutionary computation

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Genetic programming convergence

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2022年第1期23卷 71-104页

作者： Langdon, W. B. UCL Dept Comp Sci London England

We study both genotypic and phenotypic convergence in GP floating point continuous domain symbolic regression over thousands of generations. Subtree fitness variation across the population is measured and shown in many cases to fall. In an expanding region about the root node, both genetic opcodes and function evaluation values are identical or nearly identical. Bottom up (leaf to root) analysis shows both syntactic and semantic (including entropy) similarity expand from the outermost node. Despite large regions of zero variation, fitness continues to evolve and near zero crossover disruption suggests improved GP systems within existing memory use.

关键词： evolutionary computation Stochastic search Diversity Bottom up incremental evaluation PIE propagation infection and execution SIMD parallel processing AVX vector instructions

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A Multipopulation Multiobjective Ant Colony System Considering Travel and Prevention Costs for Vehicle Routing in COVID-19-Like Epidemics

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第12期23卷 25062-25076页

作者： Li, Jian-Yu Deng, Xin-Yi Zhan, Zhi-Hui Yu, Liang Tan, Kay Chen Lai, Kuei-Kuei Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China South China Normal Univ Sch Chinese Language & Literature Guangzhou 510631 Peoples R China Alibaba Feitian Campus Hangzhou 330000 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Chaoyang Univ Technol Dept Business Adm Taichung 413 Taiwan Hanyang Univ Ansan 15588 South Korea

As transportation system plays a vastly important role in combatting newly-emerging and severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing problem (VRP) in epidemics has become an emerging topic that has attracted increasing attention worldwide. However, most existing VRP models are not suitable for epidemic situations, because they do not consider the prevention cost caused by issues such as viral tests and quarantine during the traveling. Therefore, this paper proposes a multi-objective VRP model for epidemic situations, named VRP4E, which considers not only the traditional travel cost but also the prevention cost of the VRP in epidemic situations. To efficiently solve the VRP4E, this paper further proposes a novel algorithm named multiobjective ant colony system algorithm for epidemic situations, termed MOACS4E, together with three novel designs. First, by extending the efficient "multiple populations for multiple objectives" framework, the MOACS4E adopts two ant colonies to optimize the travel and prevention costs respectively, so as to improve the search efficiency. Second, a pheromone fusionbased solution generation method is proposed to fuse the pheromones from different colonies to increase solution diversity effectively. Third, a solution quality improvement method is further proposed to improve the solutions for the prevention cost objective. The effectiveness of the MOACS4E is verified in experiments on 25 generated benchmarks by comparison with six state-of- the- art and modern algorithms. Moreover, the VRP4E in different epidemic situations and a real-world case in the Beijing-Tianjin-Hebei region, China, are further studied to provide helpful insights for combatting COVID-19-like epidemics.

关键词： COVID-19 epidemics vehicle routing problem multi-objective optimization evolutionary computation ant colony system multiple populations for multiple objectives

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Seismic fragility analysis of RC box-girder bridges based on symbolic regression method

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STRUCTURES 2022年 38卷 306-322页

作者： Rezaei, Hossein Zarfam, Panam Golafshani, Emadaldin Mohammadi Amiri, Gholamreza Ghodrati Islamic Azad Univ Dept Earthquake Engn Sci & Res Branch Tehran Iran Monash Univ Dept Civil Engn Melbourne Australia Iran Univ Sci & Technol Nat Disasters Prevent Res Ctr Sch Civil Engn Tehran Iran

The fragility curves are the key tools in seismic risk assessment within the performance-based earthquake engineering framework. This study employs the Symbolic Regression (SR) analysis to develop seismic response prediction models and the fragility curves for concrete box-girder bridges. Geometrical, material, ground motion, and structural uncertainties were taken into account to improve the reliability of the derived mathematical models. The responses were recorded as Engineering Demand Parameters (EDPs) through a nonlinear time history analysis of the simulated bridges and used as targets for prediction algorithms. The parameters of higher significance were then identified based on the evolutionary correlation coefficient and adopted as input parameters. Accordingly, a total of 30 mathematical models were developed for the five classes of regular and irregular bridges and six EDPs. The model accuracy and effectiveness were evaluated based on various criteria. A method was then proposed to develop fragility curves based on the resulting prediction models. The results were suggestive of the effectiveness and accuracy of the prediction models despite their simplicity. Further, the resulting fragility curves were consistent with those obtained by nonlinear time history analysis and, for different EDPs, the geometrical parameters were among the highest-correlation parameters after the seismic intensity measure.

关键词： evolutionary computation Genetic programming Regression analysis Fragility curve Machine learning Automatic regression Multi-span bridge Nonlinear time history analysis Bridges with Unequal-Height Piers

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