Long Short Term Memory Hyperparameter Optimization for a Neural Network Based Emotion Recognition Framework by Bahareh Nakisa, Mohammad Naim Rastgoo, Andry Rakotonirainy, Frederic Maire, Vinod Chandran

“…The results also showed that evolutionary computation algorithms, particularly DE, are competitive for ensuring optimized LSTM hyperparameter values. …”
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Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming by Federico Pigozzi, Eric Medvet, Laura Nenzi

“…In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. …”
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A Large Scale Evolutionary Algorithm Based on Determinantal Point Processes for Large Scale Multi-Objective Optimization Problems by Michael Aggrey Okoth, Ronghua Shang, Licheng Jiao, Jehangir Arshad, Ateeq Ur Rehman, Habib Hamam

“…Global optimization challenges are frequent in scientific and engineering areas where loads of evolutionary computation methods i.e., differential evolution (DE) and particle-swarm optimization (PSO) are employed to handle these problems. …”
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Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm by Ali Muhammad Usman, Umi Kalsom Yusof, Syibrah Naim

“…They can easily create a balance between selected features and classification accuracy or error rate. Evolutionary computation techniques have been applied for multi-objective feature selection. …”
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A many objective commercial recommendation algorithm via Game-Based core node extraction by Yifei Sun, Yifei Cao, Shi Cheng, Jie Yang, Wenya Shi, Ao Zhang, Jiale Ju

“…In this paper, we proposed a many objective commercial recommendation algorithm that extracts the core node through the pay-off function of game theory and makes it guide the evolutionary computation. Firstly, the core aim of commercial recommendations is to maximize total profit. …”
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A novel harbor seal whiskers optimization algorithm by Hegazy Zaher, H. Al-Wahsh, M.H. Eid, Radwa S.A. Gad, Naser Abdel-Rahim, Islam M. Abdelqawee

“…To validate these abilities, the proposed HSWOA utilizes two sets of test functions: 33 benchmark functions and five IEEE Congress on Evolutionary Computation (CEC2019) benchmark functions. The results of HSWOA are compared with ten well-established optimization algorithms. …”
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Development and Comparison of Ten Differential-Evolution and Particle Swarm-Optimization Based Algorithms for Discount-Guaranteed Ridesharing Systems by Fu-Shiung Hsieh

“…Due to the complexity of the optimization problem, we adopted two evolutionary computation approaches, differential evolution and particle swarm optimization, to develop ten algorithms for solving the problem. …”
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Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification by Juan Carlos Guzmán, Ivette Miramontes, Patricia Melin, German Prado-Arechiga

“…The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. …”
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Multiple Strategies Boosted Orca Predation Algorithm for Engineering Optimization Problems by Essam H. Houssein, Mohammed R. Saad, Abdelmgeid A. Ali, Hassan Shaban

“…To investigate the validity of the proposed LFOPA, it is compared with seven robust optimizers, including the improved multi-operator differential evolution algorithm (IMODE), covariance matrix adaptation evolution strategy (CMA-ES), gravitational search algorithm (GSA), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), Harris hawks optimization (HHO), and the original OPA on 10 unconstrained test functions linked to 2020 IEEE Congress on Evolutionary Computation (CEC’20). Furthermore, four different design engineering issues, including the welded beam, the tension/compression spring, the pressure vessel, and the speed reducer, are solved using the proposed LFOPA, to test its applicability. …”
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Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization by Alejandro Rodríguez-Molina, Axel Herroz-Herrera, Mario Aldape-Pérez, Geovanni Flores-Caballero, Jarvin Alberto Antón-Vargas

“…This approach utilizes metaheuristics from evolutionary computation and swarm intelligence to find the Bug0 parameters when the mobile robot is approaching an obstacle. …”
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MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection by Junliang Shang, Yiting Li, Yan Sun, Feng Li, Yuanyuan Zhang, Jin-Xing Liu

“…Among them, evolutionary computation based ones with a single-objective function are promising in either benchmark or real data sets. …”
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Efficient Opponent Exploitation in No-Limit Texas Hold’em Poker: A Neuroevolutionary Method Combined with Reinforcement Learning by Jiahui Xu, Jing Chen, Shaofei Chen

“…Our method contains a hybrid framework that uses NE’s advantage of evolutionary computation with a long-term fitness metric to address the sparse rewards feedback in NLTH and retains RL’s gradient-based method for higher learning efficiency. …”
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A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features by Chen Li, Zhenbo Li, Jing Wu, Ling Zhu, Jun Yue

“…The fluctuation of low frequency term is gentle and periodic, so it can be modeled by BP neural network with an optimal mind evolutionary computation (MEC-BP). Then, the trend term is predicted using grey model (GM) because it is nearly linear. …”
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Improvement of an Adaptive Robot Control by Particle Swarm Optimization-Based Model Identification by Hazem Issa, József K. Tar

“…It is shown that even very primitive, fast, and simple versions of evolutionary computation-based methods can produce considerable improvement in their operation. …”
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A Competitive Memory Paradigm for Multimodal Optimization Driven by Clustering and Chaos by Jorge Gálvez, Erik Cuevas, Krishna Gopal Dhal

“…Evolutionary Computation Methods (ECMs) are proposed as stochastic search methods to solve complex optimization problems where classical optimization methods are not suitable. …”
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Probability and Certainty in the Performance of Evolutionary and Swarm Optimization Algorithms by Nikola Ivković, Robert Kudelić, Matej Črepinšek

“…Reporting the empirical results of swarm and evolutionary computation algorithms is a challenging task with many possible difficulties. …”
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An Innovative Algorithm-Driven Optimization Framework for Landfill Mining: Aiming Sustainable Profitability Expeditiously by Pooja Bhakuni, Amrit Das

“…The GAOR’s performance has been verified using the Congress on evolutionary computation (CEC) 2020 multi-objective benchmark test functions. …”
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Decomposition-Based Multi-Objective Evolutionary Algorithm Design Under Two Algorithm Frameworks by Lie Meng Pang, Hisao Ishibuchi, Ke Shang

“…The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. Over the years, many EMO algorithms have been proposed. …”
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An Implementation of Self-Organizing Maps for Airfoil Design Exploration via Multi-Objective Optimization Technique by SungKi Jung, Won Choi, Luiz S. Martins-Filho, Fernando Madeira

“…In addition, the Self-Organizing Maps, based on the neural network, are used to visualize trade-offs of a relationship between the objective function space and the design variable space obtained by evolutionary computation. The Self-Organizing Maps that can be considered as data mining of the engineering design generate clusters of object functions and design variables as an essential role of trade-off studies. …”
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Modeling Organizational Performance with Machine Learning by Jozsef Pap, Csaba Mako, Miklos Illessy, Norbert Kis, Amir Mosavi

“…The present study investigates the essential intra- and extra-organizational factors in determining the performance of firms using the European Company Survey (ECS) 2019 framework. The evolutionary computation method of genetic algorithm and the machine learning method of Bayesian additive regression trees (BART), are used to model the importance of each of the intra- and extra-organizational factors in identifying the firms’ performance as well as employee well-being. …”
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