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CreativeStone: A Creativity Booster for Hearthstone Card Decks

IEEE TRANSACTIONS ON GAMES 2024年第1期16卷 214-224页

作者： Franca, Celso Zhou, Zisen da Silva, Carolina Fernanda Santos, Cibele Simoes de Oliveira Ferreira, Lucas Braga Pereira, Lucas Henrique de Almeida, Marcos Pablo Souza Oliveira, Marina Iolanda Damasio, Thais Regina Pacheco, Vinicius Silva Goes, Fabricio Univ Fed Minas Gerais Comp Sci BR-31270901 Belo Horizonte MG Brazil Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England Pontificia Univ Catolica Minas Gerais BR-31270901 Belo Horizonte MG Brazil

Digital collectible card games (DCCG) rely on human expertise to create competitive card decks. The most effective decks are shared and become popular among players through online communities. Players then keep enhancing those decks by replacing cards and testing those new deck variations in online arenas. This fine-tuning process is time-consuming and most of the time leads to small improvements in the players win rate. This article presents CreativeStone, a creativity booster for Hearthstone card decks. Our creativity-guided approach uses a genetic algorithm and the regent-dependent creativity metric to identify the core cards of an existing deck, and then improves it toward a more valuable and novel deck by adding cards that are synergic to the core cards, but also different from the ones in the original deck. Our experimental results show that CreativeStone can boost even legendary decks, outperforming handcrafted ones by 21% in win rate.

关键词： Computational creativity digital collectible card games evolutionary algorithms hearthstone regent-dependent creativity

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Exploring Sensor Placement Optimization in Point Cloud-Derived Environment Models

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IEEE SENSORS JOURNAL 2024年第16期24卷 26574-26583页

作者： Banov, Marina Pincic, Domagoj Lenac, Kristijan Susanj, Diego Univ Rijeka Fac Engn Rijeka 51000 Croatia Univ Rijeka Ctr Artificial Intelligence & Cybersecur Rijeka 51000 Croatia Univ Rijeka Fac Engn Rijeka 51000 Croatia Juraj Dobrila Univ Pula Fac Engn Pula 52100 Croatia

In this study, we present a novel method for creating an environment model suitable for addressing the sensor placement problem. We extract a detailed environment model from a 3-D point cloud by identifying spatial boundaries and furniture in indoor spaces and representing them as a series of polygons. To validate our method, we compare its performance against ground-truth data, demonstrating high accuracy in both simple and complex environments. Subsequently, we use the obtained models in a comprehensive experiment that evaluates the effectiveness of six metaheuristic optimization algorithms in solving the sensor placement problem. We examine how the choice of optimization algorithm and the number of sensors impacts the achieved coverage through statistical analysis. With this study, we gain insights into the comparative effectiveness of various evolutionary algorithms in enhancing sensor network design within indoor spaces. In particular, the artificial bee colony (ABC) algorithm consistently delivered superior results.

关键词： Sensors Point cloud compression Solid modeling Sensor placement Three-dimensional displays Shape Cameras Environment modeling evolutionary algorithms point clouds sensor networks sensor placement

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Optimization of Distributed Energy Resources Operation in Green Buildings Environment

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SENSORS 2024年第14期24卷 4742页

作者： Ali, Safdar Hayat, Khizar Hussain, Ibrar Khan, Ahmad Kim, Dohyeun Univ Lahore Dept Software Engn Main Campus Lahore 54590 Pakistan Shinawatra Univ Fac Engn & Informat Technol Bangtoey 12160 Pathum Thani Thailand COMSATS Univ Islamabad Dept Comp Sci Abbottabad Campus Abbottabad 22060 Pakistan Jeju Natl Univ Dept Comp Engn Jejusi 63243 Jeju Do South Korea

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA-GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light

关键词： energy management evolutionary algorithms green buildings occupants comfort index energy resources prediction

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An assets maintenance-workforce planning problem under uncertainty: A chance constraints assisted simulation-optimization approach

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SIMULATION MODELLING PRACTICE AND THEORY 2024年 130卷

作者： Akl, Amany M. Rahman, Humyun Fuad Chakrabortty, Ripon K. El Sawah, Sondoss Univ New South Wales Capabil Syst Ctr Sch Syst & Comp Northcott Dr Campbell ACT South Africa Newcastle Univ Business Sch Barrack Rd Newcastle Upon Tyne NE1 4SE England UNSW Canberra ADFA Sch Syst & Comp Northcott Dr Canberra ACT 2612 Australia Univ Canberra Sch Informat Technol & Syst 11 Kirinari St Canberra ACT 2617 Australia Cardiff Metropolitan Univ Cardiff Sch Management Western Ave Cardiff CF5 2YB Wales

Preventive and corrective maintenance -workforce planning for assets is a growing area of research, given the presence of uncertainty. We model a Simulation -Optimization framework to solve this challenging large-scale maintenance -workforce planning problem while considering its stochastic nature inside both the simulation and optimization models. A holistic simulation model is proposed through multiple modules to imitate different aspects of the maintenance process, i.e., spare facilities/storage and workforce. The workforce module represents technicians' types, skills and levels, and employment life -cycle, including recruitment, promotion, and separation/retirement. The present uncertainty in the simulation model demands its corresponding optimization model to cope with the nature of the problem and guarantee a degree of confidence level of the optimal decisions. Therefore, a Chance Constraints Programming (CCP) method is integrated with an evolutionary optimization algorithm to limit the risk level of violating the random constraints while minimizing the total maintenance cost. Discrete Event Simulation (DES) and Self -Adaptive Differential Evolution assisted with Chance Constraints (SADE -CC) algorithm are coupled to solve the current problem. The optimization model is proposed to optimize the preventive maintenance frequency and workforce planning problem while implicitly considering corrective maintenance in the simulation model. The Taguchi method performs scenario and risk analysis for multiple CC probability distributions (i.e., Weibull and Normal), confidence levels, and critical parameter combinations. The model's sensitivity analysis and managerial insights are also discussed.

关键词： evolutionary algorithms Chance constraints Assets maintenance Workforce planning Maintenance scheduling Simulation-optimization

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Designing Effective Drug Therapies Using a Multiobjective Spider-Wasp Optimizer

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BIOMIMETICS 2025年第4期10卷 219-219页

作者： Nguyen, Trong-The Dao, Thi-Kien Nguyen, Van-Thien Pham, Duc-Tinh Fuzhou Inst Technol Sch Elect Engn Fuzhou 350506 Peoples R China Hanoi Univ Ind Sch Informat & Commun Technol Hanoi 10000 Vietnam

Designing effective drug therapies requires balancing competing objectives, such as therapeutic efficacy, safety, and cost efficiency-a task that poses significant challenges for conventional optimization methods. To address this, we propose the multi-objective spider-wasp optimizer (MOSWO), a novel approach uniquely emulating the cooperative predation dynamics between spiders and wasps observed in nature. MOSWO integrates adaptive mechanisms for exploration and exploitation to resolve complex trade-offs in multiobjective drug design. Unlike existing approaches, the algorithm employs a dynamic population-partitioning strategy inspired by predator-prey interactions, enabling efficient Pareto frontier discovery. We validate MOSWO's performance through extensive experiments on synthetic benchmarks and real-world case studies spanning antiviral and antibiotic therapies. Results demonstrate that MOSWO surpasses state-of-the-art methods (NSGA-II, MOEA/D, MOGWO, and MOPSO), achieving 11% higher hypervolume scores, 8% lower inverted generational distance scores, 9% higher spread scores, a 30% faster convergence, and superior robustness against noisy biological datasets. The framework's adaptability to diverse therapeutic scenarios underscores its potential as a transformative tool for computational pharmacology.

关键词： evolutionary algorithms multiobjective spider-wasp optimizer drug therapy design biologically inspired algorithms pareto optimization

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Two-stage bidirectional coevolutionary algorithm for constrained multi-objective optimization

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SWARM AND evolutionary COMPUTATION 2025年 92卷

作者： Zhao, Shulin Hao, Xingxing Chen, Li Yu, Tingfeng Li, Xingyu Liu, Wei Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China Leiden Univ Leiden Inst Adv Comp Sci NL-2333 CA Leiden Netherlands

Objective optimization and constraint satisfaction are two primary and conflicting tasks in solving constrained multi-objective optimization problems (CMOPs). To better trade off them, this paper proposes a two-stage bidirectional coevolutionary algorithm, termed C-TBCEA, for constrained multi-objective optimization. It consists of two stages, with each concentrating on specific targets, i.e., the first stage primarily focuses on objective optimization while the second stage focuses on constraint satisfaction by employing different evolutionary strategies at each stage. Via the synergy of the two stages, a dynamic trade-off between objective optimization and constraint satisfaction can be achieved, thus overcoming the distinctive challenges that may be encountered at different stages of evolution. In addition, to take advantage of both feasible and infeasible solutions, we employ two populations, i.e., the main population that stores the non-dominated feasible solutions and the archive population that maintains the informative infeasible solutions, to prompt the bidirectional coevolution of them. To validate the effectiveness of the proposed C-TBCEA, experiments are carried out on 6 CMOP test suites and 17 real-world CMOPs. The results demonstrate that the proposed algorithm is very competitive with 9 state-of-the-art constrained multi-objective optimization evolutionary algorithms (CMOEAs).

关键词： Constrained multi-objective optimization Two-stage Bidirectional coevolution evolutionary algorithms

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Analysis of evolutionary algorithms using Multi-Objective Parameter Tuning 14

Analysis of Evolutionary Algorithms using Multi-Objective Pa...

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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Ugolotti, Roberto Cagnoni, Stefano Univ Parma Dept Informat Engn I-43100 Parma Italy

ISBN: (纸本)9781450326629

evolutionary algorithms (EAs) and other metaheuristics are greatly affected by the choice of their parameters, not only as regards the precision of the solutions found, but also for repeatability, robustness, speed of convergence, and other properties. Most of these performance criteria are often conflicting with one another. In our work, we see the problem of EAs' parameter selection and tuning as a multi-objective optimization problem, in which the criteria to be optimized are precision and speed of convergence. We propose EMOPaT (evolutionary Multi-Objective Parameter Tuning), a method that uses a well-known multi-objective optimization algorithm (NSGA-II) to find a front of non-dominated parameter sets which produce good results according to these two metrics. By doing so, we can provide three kinds of results: (i) a method that is able to adapt parameters to a single function, (ii) a comparison between Differential Evolution (DE) and Particle Swarm Optimization (PSO) that takes into consideration both precision and speed, and (iii) an insight into how parameters of DE and PSO affect the performance of these EAs on different benchmark functions.

关键词： Parameter Tuning evolutionary algorithms Multi-objective Optimization Parameter Analysis

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Evolution-Based Shape and Behavior Co-Design of Virtual Agents

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第12期30卷 7579-7591页

作者： Wang, Zhiquan Benes, Bedrich Qureshi, Ahmed H. Mousas, Christos Purdue Univ W Lafayette IN 47907 USA

We introduce a novel co-design method for autonomous moving agents' shape attributes and locomotion by combining deep reinforcement learning and evolution with user control. Our main inspiration comes from evolution, which has led to wide variability and adaptation in Nature and has significantly improved design and behavior simultaneously. Our method takes an input agent with optional user-defined constraints, such as leg parts that should not evolve or are only within the allowed ranges of changes. It uses physics-based simulation to determine its locomotion and finds a behavior policy for the input design that is used as a baseline for comparison. The agent is randomly modified within the allowed ranges, creating a new generation of several hundred agents. The generation is trained by transferring the previous policy, which significantly speeds up the training. The best-performing agents are selected, and a new generation is formed using their crossover and mutations. The next generations are then trained until satisfactory results are reached. We show a wide variety of evolved agents, and our results show that even with only 10% of allowed changes, the overall performance of the evolved agents improves by 50%. If more significant changes to the initial design are allowed, our experiments' performance will improve even more to 150%. Our method significantly improved motion tasks without changing body structures, and it does not require considerable computation resources as it works on a single GPU and provides results by training thousands of agents within 30 minutes.

关键词： Behavioral sciences Shape Optimization Robots Training Task analysis Legged locomotion evolutionary algorithms physics-based simulation reinforcement learning

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Fitness function with two rankings embedded in a push and pull search framework for constrained multi-objective optimisation problems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第3期24卷 164-175页

作者： Li, Kangshun Xu, Jiaxin Xie, Shumin Wang, Hui South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Dongguan City Univ Sch Artificial Intelligence Dongguan 523109 Peoples R China Shenzhen Inst Informat Technol Sch Software Engn Shenzhen 518055 Peoples R China

Optimising objectives and satisfying constraints present significant challenges in solving constrained multi-objective optimisation problems. In this paper, we propose an algorithm that incorporates the push-and-pull search framework and a two-ranking fitness function named ToR-PPS. The algorithm is divided into three stages: the push stage, transitional stage, and pull stage. In the push stage, the population is directed toward the unconstrained Pareto front, without consideration of constraints. In the transitional stage, a diversity expansion strategy is proposed to optimise the diversity of the population. In the pull stage, the fitness function with two rankings is utilised to pull the population toward the constrained Pareto front. Experiments are conducted to compare the algorithm with five state-of-the-art constrained multi-objective optimisation evolutionary algorithms on two benchmark suites. The results clearly illustrate the superiority and efficiency of the algorithm.

关键词： constrained multi-objective optimisation problems CMOPs evolutionary algorithms push and pull search framework transitional stage diversity

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Long-term electrical energy demand forecasting by using artificial intelligence/machine learning techniques

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ELECTRICAL ENGINEERING 2024年第4期106卷 5229-5251页

作者： Ozdemir, Gulcihan Istanbul Tech Univ Informat Inst Ayazaga Campus TR-34469 Maslak Istanbul Turkiye

Forecasting of long-term annual electricity demand is studied utilizing historical data for electrical energy consumption and socio-economic indicators-gross domestic product, population, import and export values for the case of Turkey between 1975 and 2020. A quadratic model for electrical energy consumption was applied to define the relation between the historical and predicted data. This model used metaheuristic algorithms;genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), artificial intelligence (AI) approaches;neural networks (NN), and adaptive network fuzzy inference systems (ANFIS), and machine learning (ML) applications;all models undergo testing, but the top four models-stepwise linear regression (SLR), NN, Gaussian process regression (GPR) with exponential, and GPR with squared exponential-are selected for additional research to determine the best forecasting model based on their forecasting performance. Comparing the finalized models SLR produced the best forecasting model with a mean absolute percentage error (MAPE) value of 2.36%, followed by GA with 2.97%. Turkey's yearly electrical energy consumption is projected under three possible scenarios through 2030. Finding the most appropriate forecasting model among the models studied for long-term electrical energy forecasting is ultimately the primary goal of this research. Simulations are done on the MATLAB (TM) platform.

关键词： Electrical energy forecasting Energy modeling ANNs ANFIS ML Metaheuristic algorithms evolutionary algorithms Data analysis

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