%/cs/research/crest/home1/ucacbbl/bibtex/IEEEabrv.bib processed by add_key_xplor.awk Revision: 1.3 $ 24 Sep 2022 %/tmp/cec2022.bib4 processed by add_key_xplor.awk Revision: 1.3 $ 24 Sep 2022 %$0 $Revision: 1.3 $ eden.cs.ucl.ac.uk /cs/research/crest/home1/ucacbbl/doc/refs Sat Sep 24 09:38:27 BST 2022 @INPROCEEDINGS(:2022:CEC, %xplor 24 Sep 2022 author = {}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={2022 Conference Proceedings}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to use any copyrighted component of this work in other work must be obtained from the IEEE.}, keywords = {}, doi = {doi:10.1109/CEC55065.2022.9870379}, notes = {Also known as \cite{9870379}}, ) @INPROCEEDINGS(:2022:CEC, %xplor 24 Sep 2022 author = {}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={2022 {IEEE} Congress on Evolutionary Computation ({CEC)}}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to use any copyrighted component of this work in other work must be obtained from the IEEE.}, keywords = {}, doi = {doi:10.1109/CEC55065.2022.9870329}, notes = {Also known as \cite{9870329}}, ) @INPROCEEDINGS(:2022:CEC, %xplor 24 Sep 2022 author = {}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={2022 {IEEE} Congress on Evolutionary Computation ({CEC)}}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.}, keywords = {Evolutionary computation, Codes, Servers, Libraries, Copyright protection, Advertising}, doi = {doi:10.1109/CEC55065.2022.9870418}, notes = {Also known as \cite{9870418}}, ) @INPROCEEDINGS(:2022:CEC, %xplor 24 Sep 2022 author = {}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Table of Contents}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={}, keywords = {}, doi = {doi:10.1109/CEC55065.2022.9870258}, notes = {Also known as \cite{9870258}}, ) @INPROCEEDINGS(:2022:CEC, %xplor 24 Sep 2022 author = {}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Front Page}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Presents the front cover or splash screen of the proceedings record.}, keywords = {}, doi = {doi:10.1109/CEC55065.2022.9870304}, notes = {Also known as \cite{9870304}}, ) @INPROCEEDINGS(Shi:2022:CEC, %xplor 24 Sep 2022 author = {Shaolong Shi and Yifan Chen and Qiang Liu and Jurong Ding and Qingfu Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Dynamic In Vivo Computation: Nanobiosensing from a Dynamic Optimization Perspective}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={We have recently proposed a novel framework of in vivo computation by transforming the early tumor detection into an optimization problem. In the framework, the tumor-triggered biological gradient field (BGF) provides aided knowledge for the swarm-intelligence-assisted tumor targeting process. Our previous investigations are based on the hypothesis that the BGF landscape is time-invariant, which results in a static function optimization problem. However, the properties of internal environment, such as the flow state of body fluid, will bring about time-dependent variation of BGF. Thus, we focus on dynamic in vivo computation by considering different variation patterns of BGF in this paper. A computational intelligence strategy named "swarm-based learning strategy" is proposed for overcoming the turbulence of the fitness estimation caused by the BGF variation. The in silico experiments and statistical results demonstrate the effectiveness of the proposed strategy. In addition, the above process is conducted in a three-dimensional search space, which is more realistic compared to the two-dimensional search space in our previous work.}, keywords = {In vivo, Three-dimensional displays, Fluids, Estimation, Evolutionary computation, Nanobioscience, Optimization, Tumor targeting, dynamic in vivo computation, nanorobots, swarm intelligence}, doi = {doi:10.1109/CEC55065.2022.9870332}, notes = {Also known as \cite{9870332}}, ) @INPROCEEDINGS(Dang:2022:CEC, %xplor 24 Sep 2022 author = {Truong Dang and Anh Vu Luong and Alan Wee Chung Liew and John McCall and Tien Thanh Nguyen}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Ensemble of deep learning models with surrogate-based optimization for medical image segmentation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Deep Neural Networks (DNNs) have created a breakthrough in medical image analysis in recent years. Because clinical applications of automated medical analysis are required to be reliable, robust and accurate, it is necessary to devise effective DNNs based models for medical applications. In this paper, we propose an ensemble framework of DNNs for the problem of medical image segmentation with a note that combining multiple models can obtain better results compared to each constituent one. We introduce an effective combining strategy for individual segmentation models based on swarm intelligence, which is a family of optimization algorithms inspired by biological processes. The problem of expensive computational time of the optimizer during the objective function evaluation is relieved by using a surrogate-based method. We train a surrogate on the objective function information of some populations and then use it to predict the objective values of each candidate in the subsequent populations. Experiments run on a number of public datasets indicate that our framework achieves competitive results within reasonable computation time.}, keywords = {Deep learning, Image segmentation, Computational modeling, Biological system modeling, Sociology, Linear programming, Prediction algorithms, image segmentation, deep learning, ensemble learning, particle swarm optimization, surrogate models, surrogate-assisted evolutionary algorithms}, doi = {doi:10.1109/CEC55065.2022.9870389}, notes = {Also known as \cite{9870389}}, ) @INPROCEEDINGS(Greenwood:2022:CEC, %xplor 24 Sep 2022 author = {Garrison W. Greenwood and Daniel Ashlock}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving Neural Networks for a Generalized Divide the Dollar Game}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Divide the dollar is a simpler version of a game invented by John Nash to study the bargaining problem. The generalized divide the dollar game is an n-player version. Evolutionary algorithms can be used to evolve players for this game, but it has been previously shown representation has a profound effect on the success of the evolutionary search. Representation defines both the genome and the move (search) operator used by the evolutionary algorithm. This study investigates how well two representations for a 3-player generalized divide the dollar game, one using a differential evolution move operator and the other a CMA-ES move operator, can find good players implemented as neural networks. Our results indicate both representations can evolve very good player trios, but the CMA-ES representation tends to evolve fairer players.}, keywords = {Neural networks, Genomics, Games, Evolutionary computation, Bioinformatics}, doi = {doi:10.1109/CEC55065.2022.9870386}, notes = {Also known as \cite{9870386}}, ) @INPROCEEDINGS(Long:2022:CEC, %xplor 24 Sep 2022 author = {Xinpeng Long and Michael Kampouridis and Delaram Jarchi}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An in-depth investigation of genetic programming and nine other machine learning algorithms in a financial forecasting problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Machine learning (ML) techniques have shown to be useful in the field of financial forecasting. In particular, genetic programming has been a popular ML algorithm with proven success in improving financial forecasting. Meanwhile, the performance of such ML algorithms depends on a number of factors including data analysis from different markets, data periods, forecasting days ahead, and the transaction cost which have been neglected in most previous studies. Therefore, the focus of this paper is on investigating the effect of such factors. We perform an extensive evaluation of a financial genetic programming-based approach and compare its performance against 9 popular machine learning algorithms and the buy and hold trading strategy. Experiments take place over daily data from 220 datasets from 10 international markets. Results show that genetic programming not only provides profitable results but also outperforms the 9 machine learning algorithms in terms of risk and Sharpe ratio.}, keywords = {Machine learning algorithms, Data analysis, Costs, Genetic programming, Machine learning, Evolutionary computation, Benchmark testing, Genetic programming, Machine learning, Financial forecasting, Algorithmic trading}, doi = {doi:10.1109/CEC55065.2022.9870351}, notes = {Also known as \cite{9870351}}, ) @INPROCEEDINGS(Toda:2022:CEC, %xplor 24 Sep 2022 author = {Arisa Toda and Satoru Hiwa and Kensuke Tanioka and Tomoyuki Hiroyasu}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Visualization, Clustering, and Graph Generation of Optimization Search Trajectories for Evolutionary Computation Through Topological Data Analysis: Application of the Mapper}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Topological Data Analysis (TDA) is an analytical technique that can reveal the skeletal structure inherent in complex or high-dimensional data. In this study, we considered the optimization search trajectories obtained from multiple trials of evolutionary computation as a single data set and challenged to represent the similarities and differences of each search trajectory as a topological network. Mapper is one of TDA tools and it includes the dimensionality reduction of data and clustering during graph generation. We modified Mapper to apply into this problem. The proposed framework is Mapper for evolutionary computation (EvoMapper). In the numerical experiments, multiple searches were conducted at different initial points to provide a basic review of the effectiveness of EvoMapper. The test functions were the One-max and Rastrigin function. A graph providing intuitive insights on the analysis results was constructed and visualized. In addition, the trials that reached the optimal solution and those that did not were clustered and found to have similar topology.}, keywords = {Data analysis, Shape, Network topology, Perturbation methods, Sociology, Data visualization, Trajectory, Topological data analysis, Mapper, Evolutionary computing, genetic algorithms}, doi = {doi:10.1109/CEC55065.2022.9870341}, notes = {Also known as \cite{9870341}}, ) @INPROCEEDINGS(Du:2022:CEC, %xplor 24 Sep 2022 author = {Xinyang Du and Ruibin Bai and Tianxiang Cui and Rong Qu and Jiawei Li}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={A competitive traveling salesmen problem is a variant of traveling salesman problem in that multiple agents compete with each other in visiting a number of cities. The agent who is the first one to visit a city will receive a reward. Each agent aims to collect as more rewards as possible with the minimum traveling distance. There is still not effective algorithms for this complicated decision making problem. We investigate an improved ant colony approach for the competitive traveling sales-men problem which adopts a time dominance mechanism and a revised pheromone depositing method to improve the quality of solutions with less computational complexity. Simulation results show that the proposed algorithm outperforms the state of art algorithms.}, keywords = {Machine learning algorithms, Simulation, Computational modeling, Urban areas, Decision making, Machine learning, Games, Ant colony, competitive traveling salesmen problem, heuristic, algorithm}, doi = {doi:10.1109/CEC55065.2022.9870414}, notes = {Also known as \cite{9870414}}, ) @INPROCEEDINGS(Greensmith:2022:CEC, %xplor 24 Sep 2022 author = {Julie Greensmith and Longzhi Yang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={{TwoDCA:} A 2-Dimensional Dendritic Cell Algorithm with Dynamic Cell Migration}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The Dendritic Cell Algorithm (DCA) is a multi-agent artificial immune system designed for anomaly detection. The algorithm is composed of artificial dendritic cell agents that process timestamped stream data. The lifespan of a cell agent is determined by its migration threshold, which influences the algorithm's dynamics significantly. The migration threshold is fixed during cell initialisation which limits the performance on various problems. This work proposes a dynamic migration threshold adjustment mechanism by mapping the population to a 2D grid and using Von Neumann Neighbourhoods to adapt this parameter at run time. This forms a novel algorithm variant termed 'twoDCA', implemented using the Repast Simphony agent based java API. This new algorithm is applied on synthetic stream data using a sin function generator with two different ways of migration threshold parameter generation. The experimental results show that the introduction of the Von Neumann Neigh-bourhoods has led to a statistically significant impact on certain behaviours of the algorithm. In particular, the great dynamics of twoDCA is realised by carrying forward the updated migration thresholds between cell reincarnations. The twoDCA is readily applicable to 2D data streams, which will diversify the range of applications substantially to which the algorithm can be applied and yields opportunities to add learning components to the core functionality of the algorithm.}, keywords = {Java, Heuristic algorithms, Artificial immune systems, Sociology, Evolutionary computation, Signal generators, Statistics, Artificial Immune Systems, Dendritic Cell Al-gorithm, Migration Threshold, Von Neumann Neighbourhood}, doi = {doi:10.1109/CEC55065.2022.9870441}, notes = {Also known as \cite{9870441}}, ) @INPROCEEDINGS(Starodubcev:2022:CEC, %xplor 24 Sep 2022 author = {Nikita O. Starodubcev and Nikolay O. Nikitin and Anna V. Kalyuzhnaya}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convo-lutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows to obtain more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time.}, keywords = {Solid modeling, Costs, Computational modeling, Neural networks, Sea measurements, Evolutionary computation, Predictive models, Multi-objective surrogate-assisted optimization, evolutionary algorithms, convolutional neural network, generative design of breakwaters, numerical simulation}, doi = {doi:10.1109/CEC55065.2022.9870336}, notes = {Also known as \cite{9870336}}, ) @INPROCEEDINGS(Ishibuchi:2022:CEC, %xplor 24 Sep 2022 author = {Hisao Ishibuchi and Yiming Peng and Lie Meng Pang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-Modal Multi-Objective Test Problems with an Infinite Number of Equivalent Pareto Sets}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Multi-modal multi-objective optimization problems have multiple equivalent Pareto sets, each of which is mapped to the entire Pareto front. A number of multi-modal multi-objective algorithms have been proposed to find all equivalent Pareto sets. Their performance is evaluated by computational experiments on multi-modal multi-objective test problems. A common feature of those test problems is that a single point on the Pareto front in the objective space corresponds to multiple clearly separated Pareto optimal solutions in the decision space. In this paper, we propose a new type of multi-modal multi-objective test problems where a single point on the Pareto front corresponds to an infinite number of Pareto optimal solutions (i.e., a subset of the decision space). This means that the mapping from the Pareto set in the decision space to the Pareto front in the objective space is a set-to-point mapping. For example, all points on a line in the decision space are mapped to the same single point on the Pareto front. As a result, the dimensionality of the Pareto set is larger than that of the Pareto front. We examine the search behavior of multi-modal multi-objective algorithms using the proposed test problems. Some interesting observations are reported.}, keywords = {Evolutionary computation, Pareto optimization, Search problems, Behavioral sciences, Optimization, Multi-objective optimization, multi-modal multi-objective optimization, test problems, equivalent Pareto sets}, doi = {doi:10.1109/CEC55065.2022.9870307}, notes = {Also known as \cite{9870307}}, ) @INPROCEEDINGS(Palazzolo:2022:CEC, %xplor 24 Sep 2022 author = {Thomas Palazzolo and Chencheng Zhang and Sarah Saad and Robert G. Reynolds}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Learning the Impact of Group Structure on Optimal Herd Path Planning with Cultural Algorithms}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper compares several approaches to cooperative multi-agent path planning (MAP) based upon variations of the A* algorithm. To simulate multi-agent migration patterns three path-finding mechanisms based on the classic A* algorithm was utilized: A*; A*, Ambush and Dendriform. Each makes different assumptions about group leadership in terms of their path generation. A* assumes a single leader for the migratory group: A*mbush allows the group to move in waves; and Dendriform allows the group to decompose and recompose into groups of arbitrary size with local leaders. Each mechanism required parameter weightings so that the simulated agents would interact realistically with their environment. Cultural Algorithms were employed to adjust the parameter weight categories in order to optimize the group movement under each of these leadership strategies. The three approaches were applied to the simulation of a real-world multi-agent system, the migration of large herd of caribou. The simulated migration was part of the Deepdive Virtual Reality system. In those simulations A* with a single planning agent emphasized nutrition at the expense of the other parameters. A*mbush learned to reduce nutrition slightly and while increasing its emphasis on risk and exploration. On the other hand, Dendriform emphasized overall effort since its planning more dynamic and required more concentration on local effort to be optimized.}, keywords = {Solid modeling, Leadership, Virtual reality, Organizations, Path planning, Planning, Cultural differences, Cultural Algorithms, Evolutionary Learning, Swarm Intelligence, Virtual Reality, Cooperative Multi-Agent Path Planning}, doi = {doi:10.1109/CEC55065.2022.9870381}, notes = {Also known as \cite{9870381}}, ) @INPROCEEDINGS(Navarro:2022:CEC, %xplor 24 Sep 2022 author = {Mario A. Navarro and Alfonso Ramos-Michel and Bernardo Morales-Castaneda and Oscar Maciel-Castillo and Itzel Aranguren and Arturo Valdivia and Diego Oliva and Seyed Jalaleddin Mousavirad}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Improving the optimization performance by an adaptable design: A dynamic selection of operators via criteria-based matrix for evolutionary algorithms}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The balance between exploration and exploitation is an important feature in Evolutionary Algorithms (EA). The use of different operators permits to explore the search space and exploit the most prominent regions. This article introduces a dynamic operator selection method that considers different criteria at the same time. The proposed approach uses a dynamic decision matrix (DyDM) to identify which operators must be used at each iteration based on how the algorithm behaves. The DyDM considers specific information as the diversity of the algorithm to avoid stagnation, the actual iteration to work accordingly, and the fitness to direct the search. The proposed approach is called Dynamic Decision Matrix Optimizer (DyDMO) and it has been compared with different well-known algorithms tested on the CEC 2017 benchmark functions. The comparative analysis and non-parametric statistical tests validate how DyDMO im-proves the quality of the solutions and is more stable than its comnetitors.}, keywords = {Heuristic algorithms, Sociology, Metaheuristics, Decision making, Evolutionary computation, Benchmark testing, Space exploration, Decision Matrix, Operator selection, Diversity, Evolutionary operators}, doi = {doi:10.1109/CEC55065.2022.9870316}, notes = {Also known as \cite{9870316}}, ) @INPROCEEDINGS(Christodoulaki:2022:CEC, %xplor 24 Sep 2022 author = {Eva Christodoulaki and Michael Kampouridis}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={U sing strongly typed genetic programming to combine technical and sentiment analysis for algorithmic trading}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Algorithmic trading has become an increasingly thriving research area and a lot of focus has been given on indicators from technical and sentiment analysis. In this paper, we examine the advantages of combining features from both analyses. To do this, we use two different genetic programming algorithms (GP). The first algorithm allows trees to contain technical and/or sentiment analysis indicators without any con-straints. The second algorithm introduces technical and sentiment analysis types through a strongly typed GP, whereby one branch of a given tree contains only technical analysis indicators and another branch of the same tree contains only sentiment analysis features. This allows for better exploration and exploitation of the search space of the indicators. We perform experiments on 10 international stocks and compare the above two GPs' performances. Our goal is to demonstrate that the combination of the indicators leads to improved financial performance. Our results show that the strongly typed GP is able to rank first in terms of Sharpe ratio and statistically outperform all other algorithms in terms of rate of return.}, keywords = {Sentiment analysis, Genetic programming, Evolutionary computation, Technical Analysis, Sentiment Analysis, Genetic Programming, Algorithmic Trading}, doi = {doi:10.1109/CEC55065.2022.9870240}, notes = {Also known as \cite{9870240}}, ) @INPROCEEDINGS(Morales-Castaneda:2022:CEC, %xplor 24 Sep 2022 author = {Bernardo Morales-Castaneda and Oscar Maciel-Castillo and Mario A. Navarro and Itzel Aranguren and Arturo Valdivia and Alfonso Ramos-Michel and Diego Oliva and Salvador Hinojosa}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Handling stagnation through diversity analysis: A new set of operators for evolutionary algorithms}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Population size is an important variable in evolutionary algorithms (EA). Its proper configuration improves the performance of the search process not only in terms of the fitness function but also for the resources required. This article introduced a population management mechanism that includes different operators. Such operators are designed and applied based on the diversity of the population. In general terms, the operators address problems in EA regarding stagnation and the inefficient use of the function evaluations. As a case of study, the proposed method is applied in the Differential Evolution (DE) to provide it the ability to change its population size according to its needs. The experimental results and comparisons demonstrate greatly improved performance when compared to the unmodified DE, some of its most successful variants, and other much more complex algorithms from the state-of-the-art.}, keywords = {Graphics, Sociology, Metaheuristics, Refining, Evolutionary computation, Benchmark testing, Search problems, Metaheuristics, Exploration, Exploitation, Di-versity, Evolutionary algorithms, Population}, doi = {doi:10.1109/CEC55065.2022.9870284}, notes = {Also known as \cite{9870284}}, ) @INPROCEEDINGS(Zhang:2022:CEC, %xplor 24 Sep 2022 author = {Wanting Zhang and Wei Du and Guo Yu and Renchu He and Wenli Du}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Large-scale crude oil scheduling: A framework of hybrid optimization based on plan decomposition}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In large refineries, the resource-oriented plan of crude oil is commonly required to be tractable and decomposable for practical operation scheduling, especially for large-scale scheduling. To this end, a framework of hybrid optimization based on plan decomposition (FHO/PD) is proposed, which mainly depends on evolutionary algorithms to realize the flexible decomposition from large-scale planning to scheduling and takes advantage of mathematical programming to improve the solving efficiency synchronously. Finally, the experimental results on a practical case suggest that the proposed method has shown great flexibility and applicability in crude oil scheduling.}, keywords = {Schedules, Oils, Refining, Evolutionary computation, Scheduling, Optimization, Mathematical programming, Crude oil scheduling, large-scale, decomposition for plans, evolutionary algorithm (EA)}, doi = {doi:10.1109/CEC55065.2022.9870364}, notes = {Also known as \cite{9870364}}, ) @INPROCEEDINGS(Liu:2022:CEC, %xplor 24 Sep 2022 author = {Peng Liu and Zheng Guo and Hua Yu and Han Linghu and Yijing Li and Yaqing Hou and Hongwei Ge and Qiang Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Preliminary Study of Multi-task {MAP-Elites} with Knowledge Transfer for Robotic Arm Design}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The structure design of robotic arms is of great importance on completing industrial tasks successfully. This is a typical multi-task optimization problem when considering different constraints as different tasks. However, mainstream methods for multi-task optimization such as evolutionary multitasking and Multi-task MAP-Elites algorithms tend to encounter problems such as high computational cost and slow convergence when solving large-scale robotic arm tasks. To this end, this paper proposes a new framework based on the MAP-Elites algorithms for solving large-scale robot arm design tasks, called Multi-task MAP-Elites with Knowledge Transfer (MMKT). Specifically, this paper designs the group-based knowledge transfer process for large-scale task optimization in which all tasks are classified into different groups according to their similarity to generate multiple knowledge transfer areas; and knowledge transfer strategies are designed to enhance the quality of solutions with low fitness value. We test the effectiveness of the MMKT framework in planar robotic arm experiments (2000, 5000, and 10,000 tasks; 10, 15-dimensional search space). The experimental results prove that the MMKT outperforms the MME, CMA-ES, and classical ES algorithms.}, keywords = {Service robots, Evolutionary computation, Multitasking, Manipulators, Computational efficiency, Complexity theory, Task analysis, Robotic Arm Design, Multi-task Optimization, MFEA, QD algorithm, Knowledge Transfer}, doi = {doi:10.1109/CEC55065.2022.9870374}, notes = {Also known as \cite{9870374}}, ) @INPROCEEDINGS(Colombelli:2022:CEC, %xplor 24 Sep 2022 author = {Felipe Colombelli and Vitor Kehl Matter and Bruno Iochins Grisci and Leomar Lima and Karine Heinen and Marcio Borges and Sandro Jose Rigo and Jorge Luis Victoria Barbosa and Rodrigo {Da Rosa Righi} and Cristiano Andre {Da Costa} and Gabriel {De Oliveira Ramos}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective prioritization for data center vulnerability remediation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Nowadays, one of the most relevant challenges of a data center is to keep its information secure. To avoid data leaks and other security problems, data centers have to manage vulnerabilities, including determining the higher-risk vulnerabilities to prioritize. However, the current literature is scarce in the proposal of intelligent methods for the complex problem of vulnerabilities prioritization. Depending on the adopted metrics, the priority could shift, compromising simple sorting-based approaches and impairing the utilization of conflicting risk assessment metrics. Unlike the related work, this study proposes a multi-objective method that uses user-chosen vulnerabilities assessment metrics to output a complete list of these vulnerabilities ranked by their risk and overall impact in the context of an organization. The method includes a multi-objective large-scale optimization problem representation, a novel population initialization scheme, an expressive fitness function, a post-optimization process, and a custom way to select the best solution among the non-dominated ones. The dataset used in the experiments contains anonymized real-world information about database vulnerabilities obtained from a private organization. The experiments' results indicated that the proposed method can reduce the number of vulnerabilities needed to reach an organization's predefined security targets compared to the baselines simulating a security team's analysis. Multi-objective optimization achieved on average a 48,1percent reduction in the vulnerabilities needed to reach the organization's target values compared to the baselines.}, keywords = {Measurement, Data centers, Sociology, Organizations, Security, Risk management, Proposals, vulnerabilities, multi-objective optimization, database security, risk prioritization}, doi = {doi:10.1109/CEC55065.2022.9870289}, notes = {Also known as \cite{9870289}}, ) @INPROCEEDINGS(Stanovov:2022:CEC, %xplor 24 Sep 2022 author = {Vladimir Stanovov and Shakhnaz Akhmedova and Eugene Semenkin}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={{NL-SHADE-LBC} algorithm with linear parameter adaptation bias change for CEC 2022 Numerical Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this paper the adaptive differential evolution algorithm is presented, which includes a set of concepts, such as linear bias change in parameter adaptation, repetitive generation of points for bound constraint handling, as well as non-linear population size reduction and selective pressure. The proposed algorithm is used to solve the problems of the CEC 2022 Bound Constrained Single Objective Numerical Optimization bench-mark problems. The computational experiments and analysis of the results demonstrate that the NL-SHADE-LBC algorithm presented in this study is able to demonstrate high efficiency in solving complex optimization problems compared to the winners of the previous years' competitions.}, keywords = {Constraint handling, Sociology, Evolutionary computation, Benchmark testing, Computational efficiency, Statistics, Optimization, optimization, differential evolution, parameter adaptation, CEC 2022}, doi = {doi:10.1109/CEC55065.2022.9870295}, notes = {Also known as \cite{9870295}}, ) @INPROCEEDINGS(Wang:2022:CEC, %xplor 24 Sep 2022 author = {Yi Wang and Tao Li and Xiaojie Liu and Jian Yao}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A discrete clonal selection algorithm for filter-based local feature selection}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Feature selection algorithms aim to improve the per-formance of machine learning algorithms by removing irrelevant and redundant features. Various feature selection algorithms have been proposed, but most of them select a global feature subset for characterizing the entire sample space. In contrast, this study proposes an efficient discrete clonal selection algorithm for local feature selection called DCSA-LFS with three features: (1) local sample behaviors are considered, and a local clustering-based evaluation criterion is used to select a distinct optimized feature subset for each different sample region; (2) an improved discrete clonal selection algorithm is proposed, which uses a differential evolution-based mutation operator to enhance the search capability of clonal selection algorithms; and (3) a two-part antibody representation is adopted to automatically adjust the weight-related parameter. Experimental results on twelve UCI datasets show that DCSA-LFS is competitive with traditional filter-based feature selection algorithms and a clonal selection algorithm-based local feature selection algorithm.}, keywords = {Machine learning algorithms, Clustering algorithms, Evolutionary computation, Filtering algorithms, Feature extraction, Behavioral sciences, Classification algorithms, local feature selection, clonal selection algorithm, artificial immune system, dimensionality reduction}, doi = {doi:10.1109/CEC55065.2022.9870318}, notes = {Also known as \cite{9870318}}, ) @INPROCEEDINGS(Lin:2022:CEC, %xplor 24 Sep 2022 author = {Baihan Lin}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={As two popular schools of machine learning, online learning and evolutionary computations have become two important driving forces behind real-world decision making engines for applications in biomedicine, economics, and engineering fields. Although there are prior work that utilizes bandits to improve evolutionary algorithms' optimization process, it remains a field of blank on how evolutionary approach can help improve the sequential decision making tasks of online learning agents such as the multi-armed bandits. In this work, we propose the Genetic Thompson Sampling, a bandit algorithm that keeps a population of agents and update them with genetic principles such as elite selection, crossover and mutations. Empirical results in multi-armed bandit simulation environments and a practical epidemic control problem suggest that by incorporating the genetic al-gorithm into the bandit algorithm, our method significantly outperforms the baselines in nonstationary settings. Lastly, we in-troduce EvoBandit, a web-based interactive visualization to guide the readers through the entire learning process and perform lightweight evaluations on the fly. We hope to engage researchers into this growing field of research with this investigation.11The data and codes to reproduce the empirical results can be accessed and reproduced at https://github.com/doerlbh/BanditZoo.}, keywords = {Epidemics, Decision making, Sociology, Evolutionary computation, Machine learning, Genetics, Task analysis, Multi-armed bandits, genetic algorithm}, doi = {doi:10.1109/CEC55065.2022.9870279}, notes = {Also known as \cite{9870279}}, ) @INPROCEEDINGS(Van-Zyl:2022:CEC, %xplor 24 Sep 2022 author = {Jean-Pierre {Van Zyl} and Andries P. Engelbrecht}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Rule Induction Using Set-Based Particle Swarm Optimisation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper presents a new approach to induce a list of rules from a dataset by using a set-based particle swarm optimisation algorithm. Many contemporary rule induction algorithms tend to use similar information gain based approaches to fit a training dataset. The proposed novel algorithm is a meta-heuristic approach which finds an optimal rule list while providing the flexibility to overcome traditional drawbacks such as overfitting and rigidity to the datatypes that can be used. This paper shows that the proposed algorithm performs comparatively well when compared to existing rule induction algorithms and it has the potential to be expanded further by adding rule pruning techniques.}, keywords = {Training, Metaheuristics, Rigidity, Particle swarm optimization, rule induction, particle swarm optimisation, set-based particle swarm optimisation}, doi = {doi:10.1109/CEC55065.2022.9870360}, notes = {Also known as \cite{9870360}}, ) @INPROCEEDINGS(Chen:2022:CEC, %xplor 24 Sep 2022 author = {Stephen Chen and Antonio Bolufe-Rohler and James Montgomery and Wenxuan Zhang and Tim Hendtlass}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Using Average-Fitness Based Selection to Combat the Curse of Dimensionality}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={It is well known that metaheuristics for numerical optimization tend to decrease in performance as dimensionality increases. These effects are commonly referred to as "The Curse of Dimensionality". An obvious change to search spaces with increasing dimensionality is that their volume grows exponentially, and this has led to large amounts of research on improved exploration. A recent insight is that the shape of attraction basins can also change drastically with increasing dimensionality, and this has led to selection-based approaches to combat the Curse of Dimensionality. Average-Fitness Based Selection is introduced as a means to reduce the selection errors caused by Fitness-Based Selection. Experimental results show that the rate of selection errors grows much more slowly for Average-Fitness Based Selection with Increasing dimensionality.}, keywords = {Shape, Metaheuristics, Evolutionary computation, Space exploration, selection, exploration, metaheuristic, curse of dimensionality}, doi = {doi:10.1109/CEC55065.2022.9870232}, notes = {Also known as \cite{9870232}}, ) @INPROCEEDINGS(Chen:2022:CEC, %xplor 24 Sep 2022 author = {Stephen Chen and Antonio Bolufe-Rohler and James Montgomery and Dania Tamayo-Vera and Tim Hendtlass}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Measuring the Effects of Increasing Dimensionality on Fitness-Based Selection and Failed Exploration}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The rate of Successful Exploration is related to the proportion of search solutions from fitter attraction basins that are fitter than the current reference solution. A reference solution that moves closer to its local optimum (i.e. experiences exploitation) will reduce the proportion of these fitter solutions, and this can lead to decreased rates of Successful Exploration/increased rates of Failed Exploration. This effect of Fitness-Based Selection is studied in Particle Swarm Optimization and Differential Evolution with increasing dimensionality of the search space. It is shown that increasing rates of Failed Exploration represent another aspect of the Curse of Dimensionality that needs to be addressed by metaheuristic design.}, keywords = {Atmospheric measurements, Metaheuristics, Particle measurements, Particle swarm optimization, Exploration, Exploitation, Fitness-Based Selection, Curse of Dimensionality, Particle Swarm Optimization, Differential Evolution}, doi = {doi:10.1109/CEC55065.2022.9870409}, notes = {Also known as \cite{9870409}}, ) @INPROCEEDINGS(Yadollahpour:2022:CEC, %xplor 24 Sep 2022 author = {Naeemeh Yadollahpour and Stephen Chen}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Methods to Detect and Address Stall in Particle Swarm Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Particle Swarm Optimization can experience stall in multi-modal search spaces. A stalled swarm is unable to converge and unable to find better solutions because all of its exploratory search solutions are rejected. Remedies to address the stall condition can benefit from knowing the particles that are still performing exploration, so we develop an efficient and accurate real-time, search space independent method to identify these particles. We confirm the benefit of identifying stalled particles through a modification designed for globally convex search spaces. We also discuss opportunities to differentiate search space landscapes and propose future research that can address non-globally convex search spaces.}, keywords = {Atmospheric measurements, Particle measurements, Real-time systems, Particle swarm optimization, Convergence, exploration, selection, convergence, particle swarm optimization}, doi = {doi:10.1109/CEC55065.2022.9870413}, notes = {Also known as \cite{9870413}}, ) @INPROCEEDINGS(Liang:2022:CEC, %xplor 24 Sep 2022 author = {Jing Liang and Junting Yang and Caitong Yue and Gongping Li and Kunjie Yu and Boyang Qu}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Multimodal Multiobjective Genetic Algorithm for Feature Selection}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={When performing feature selection on most data sets, there is a general situation that some different feature subsets have the same number of selected features and classification error rate. This indicates that feature selection in some data sets is a multimodal multiobjective optimization (MMO) problem. Most of the current studies on feature selection ignore the MMO problems. Therefore, this paper proposes a feature selection method based on a multimodal multiobjective genetic algorithm (MMOGA) to solve the problem. This algorithm is mainly improved in three aspects. First, a special initialization strategy based on symmetric uncertainty is designed to improve the fitness of the initial population. Second, this paper adds a niche strategy to the genetic algorithm to search for multimodal solutions. Unlike traditional niche methods that has a central individual, this algorithm also considers the distances between individuals in the niche. Third, to effectively utilize excellent individuals for evolution, this algorithm uses a method based on the Pareto set of the niche to generate offspring. Finally, by comparing with other algorithms, the effectiveness of the MMOGA in feature selection is verified. This algorithm can successfully find equivalent feature subsets on different datasets.}, keywords = {Correlation, Uncertainty, Sociology, Evolutionary computation, Feature extraction, Genetics, Encoding, Feature selection, Genetic algorithm, Multimodal multiobjective optimization}, doi = {doi:10.1109/CEC55065.2022.9870227}, notes = {Also known as \cite{9870227}}, ) @INPROCEEDINGS(Xu:2022:CEC, %xplor 24 Sep 2022 author = {Meng Xu and Fangfang Zhang and Yi Mei and Mengjie Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic Programming with Multi-case Fitness for Dynamic Flexible Job Shop Scheduling}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Dynamic flexible job shop scheduling has attracted widespread interest from scholars and industries due to its practical value. Genetic programming hyper-heuristic has achieved great success in automatically evolving effective scheduling heuristics to make real-time decisions (i.e., operation ordering and machine assignment) for dynamic flexible job shop scheduling. The design of the training set and fitness evaluation play key roles in improving the generalisation of the evolved scheduling heuristics. The commonly used strategies for improving the generalisation of learned scheduling heuristics include using multiple instances for evaluation at each generation or using a single instance but changing the instance at each new generation of the training process of genetic programming. However, using multiple instances is time-consuming, while changing a single instance at each new generation, potentially promising individuals that happen to underperform in one particular generation might be lost. To address this issue, this paper develops a genetic programming method with a multi-case fitness evaluation strategy, which is named GPMF to evolve the scheduling heuristics with better generalisation ability for the dynamic flexible job shop scheduling problem. The proposed multi-case fitness evaluation strategy divides one instance into multiple cases and uses the average value of the multi-case objectives as the fitness. Experimental results show that the proposed GPMF algorithm is significantly better than the baseline method in all the tested scenarios.}, keywords = {Training, Industries, Job shop scheduling, Heuristic algorithms, Genetic programming, Evolutionary computation, Dynamic scheduling, genetic programming, dynamic flexible job shop scheduling}, doi = {doi:10.1109/CEC55065.2022.9870340}, notes = {Also known as \cite{9870340}}, ) @INPROCEEDINGS(Xu:2022:CEC, %xplor 24 Sep 2022 author = {Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic Programming with Cluster Selection for Dynamic Flexible Job Shop Scheduling}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Dynamic flexible job shop scheduling is a challenging combinatorial optimisation problem, that aims to optimise machine resources for producing jobs to meet some goals. There are two important kinds of decisions that the scheduling process needs to make under dynamic environments, i.e., the routing decision for machine assignment and the sequencing decision for operation ordering. Genetic programming hyper-heuristic has been successfully applied for solving the dynamic flexible job shop scheduling problem with the advantage of automat-ically evolving good scheduling heuristics. Parent selection is an important process for genetic programming, intending to select good individuals as parents to generate offspring for the next generation. Traditional genetic programming methods select parents for crossover based on only fitness (e.g., tournament selection). In this paper, a new parent selection (i.e., cluster selection) method is proposed to select parents not only with good fitness but also with different behaviours. The proposed cluster selection is combined with genetic programming hyper-heuristic to study whether considering different behaviours in parent selection will improve the effectiveness of the evolved scheduling heuristics. The experimental results show that increasing the number of unique behaviours in the population cannot help evolve effective scheduling heuristics. Further analysis shows that considering behaviour to select parents does increase the number of unique behaviours in the population. However, it gives individuals with poor fitness more probability to be selected to generate offspring. This might be the reason why the proposed method cannot outperform the baseline method.}, keywords = {Sequential analysis, Job shop scheduling, Processor scheduling, Sociology, Genetic programming, Dynamic scheduling, Routing, dynamic flexible job shop scheduling, genetic programming, cluster selection, diversity}, doi = {doi:10.1109/CEC55065.2022.9870431}, notes = {Also known as \cite{9870431}}, ) @INPROCEEDINGS(Zhang:2022:CEC, %xplor 24 Sep 2022 author = {Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Learning Strategies on Scheduling Heuristics of Genetic Programming in Dynamic Flexible Job Shop Scheduling}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Dynamic flexible job shop scheduling is an important combinatorial optimisation problem that covers valuable practical applications such as order picking in warehouses and service allocation in cloud computing. Machine assignment and operation sequencing are two key decisions to be considered simultaneously in dynamic flexible job shop scheduling. Genetic programming has been successfully and widely used to learn scheduling heuristics, including a routing rule for machine assignment and a sequencing rule for operation sequencing simultaneously. There are mainly two types of learning strategies to evolve scheduling heuristics, i.e., learning one rule by fixing the other rule, and learning the routing rule and the sequencing rule simultaneously. However, there is no guidance on which learning strategy to use in specific cases. To fill this gap, this paper provides a comprehensive study of learning strategies on scheduling heuristics of genetic programming in dynamic flexible job shop scheduling by comparing five learning strategies, including two strategies that are extended from the existing studies. The results show that learning two rules simultaneously, either using cooperative coevolution or multi-tree representation, is more effective than only learning one type of rule. Cooperative coevolution is recommended if an algorithm aims to handle a problem by dividing it into small sub-problems, and focuses on the characteristics of routing rule and sequencing rule. Genetic programming with multi-tree representation that treats the routing rule and the sequencing rule as an individual, is preferred to reduce the complexities of algorithms.}, keywords = {Sequential analysis, Job shop scheduling, Processor scheduling, Heuristic algorithms, Genetic programming, Dynamic scheduling, Routing, Surrogate, Instance Rotation, Genetic Program-ming, Brood Recombination, Dynamic Job Shop Scheduling}, doi = {doi:10.1109/CEC55065.2022.9870243}, notes = {Also known as \cite{9870243}}, ) @INPROCEEDINGS(Hamza:2022:CEC, %xplor 24 Sep 2022 author = {Noha Hamza and Saber Elsayed and Ruhul Sarker and Daryl Essam}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Solving constrained problems with dynamic objective functions}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Many practical decision-making problems involve changing data and parameters with time. Solving such problems requires a custom-designed algorithm that can efficiently handle the repeatedly changing problem, in fact, its changing search space. In this paper, we consider constrained optimisation problems where the coefficients of the objective function change. We propose a framework that adaptively deals with linear and nonlinear components by satisfying the constraints within a limited time. Furthermore, we introduce a new mechanism to identify the sensitivity of variables, determine the rate of changes in the coefficients of the decision variables, and propose a heuristic to update the population efficiently after every change. The experimental results demonstrate that the proposed approach is able to obtain better solutions than those without having these new components.}, keywords = {Sensitivity, Sociology, Decision making, Evolutionary computation, Linear programming, Search problems, Proposals, constrained problems, dynamic optimization, evolutionary algorithms}, doi = {doi:10.1109/CEC55065.2022.9870354}, notes = {Also known as \cite{9870354}}, ) @INPROCEEDINGS(Liu:2022:CEC, %xplor 24 Sep 2022 author = {Qingping Liu and Tingting Pang and Kaige Chen and Zuling Wang and Weiguo Sheng}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Adaptive Multi-subpopulation based Differential Evolution for Global Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Properly configuring mutation strategies and their associated parameters in DE is inherently a difficult issue. In this paper, an adaptive multi-subpopulation based differential evolution has been proposed and employed for global optimization. In the proposed method, the entire population is firstly adaptively divided at each generation according to a devised population division strategy, which try to partition the population into multiple subpopulations according to the potential of individuals. Then, a suitable mutation strategy along with an appropriate parameter control scheme is introduced and assigned to each subpopulation for evolution, with the purpose of delivering a balanced evolution. The performance of proposed algorithm has been evaluated on CEC'2015 benchmark functions and compared with related methods. The results show that our method can outperform related methods to be compared.}, keywords = {Sociology, Evolutionary computation, Benchmark testing, Partitioning algorithms, Statistics, Optimization, Differential evolution, multi-subpopulation, mutation configuration, parameter control}, doi = {doi:10.1109/CEC55065.2022.9870398}, notes = {Also known as \cite{9870398}}, ) @INPROCEEDINGS(Pang:2022:CEC, %xplor 24 Sep 2022 author = {Tingting Pang and Jing Wei and Kaige Chen and Zuling Wang and Weiguo Sheng}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An Adaptive Differential Evolution with Mutation Strategy Pools for Global Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper proposes an adaptive differential evolution algorithm with mutation strategy pools for global optimization. In the proposed method, the mutation strategy pool mechanism is devised to supply appropriate mutation strategy for different individuals in the population. Further, a mutation strategy, called DE/current-to-wb/l, has also been designed and employed in the mutation strategy pool. The performance of the proposed algorithm has been evaluated on CEC'2014 benchmark functions and compared with related methods. Experimental results show that the proposed algorithm has a good performance and outperforms related algorithms.}, keywords = {Sociology, Evolutionary computation, Benchmark testing, Statistics, Optimization, Differential evolution, Adaptive mutation selection, Multi-strategy pools}, doi = {doi:10.1109/CEC55065.2022.9870292}, notes = {Also known as \cite{9870292}}, ) @INPROCEEDINGS(Wang:2022:CEC, %xplor 24 Sep 2022 author = {Xia Wang and Hongwei Ge and Liang Sun and Xinming Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Random Learning Particle Swarm Optimization with Quasi-Newton Exploitation Mechanism}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In traditional particle swarm optimization (PSO) algorithm, each particle updates its velocity and position with a learning mechanism based on its personal historical best position and the best population position. The learning mechanism in traditional PSO is simple and easy to implement, but it suffers some potential problems, such as being easily trapped in local optimum and insufficient balance. Thus, a novel random learning PSO with improved quasi-Newton exploitation mechanism (RQ-PSO) is proposed. Firstly, to improve the global search ability, a random learning mechanism is proposed through the analysis of PSO based on many kinds of learning mechanisms. Then, the random learning mechanism is effectively integrated into PSO to obtain strong global search ability and avoid falling into local optima. Finally, to keep a better balance between exploration and exploitation, an improved quasi-Newton method with strong exploitation ability is incorporated into RL-PSO. The experimental results on the complex functions from CEC-2013 and CEC-2017 test sets show that RQ-PSO outperforms the state-of-the-art PSO variants.}, keywords = {Learning systems, Sociology, Particle swarm optimization, Statistics, Intelligent optimization, particle swarm optimization, random learning, quasi-Newton exploitation}, doi = {doi:10.1109/CEC55065.2022.9870367}, notes = {Also known as \cite{9870367}}, ) @INPROCEEDINGS(Butt:2022:CEC, %xplor 24 Sep 2022 author = {Muhammad Hassaan Farooq Butt and Hamail Ayaz and Muhammad Ahmad and Jian Ping Li and Ramil Kuleev}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Fast and Compact Hybrid {CNN} for Hyperspectral Imaging-based Bloodstain Classification}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In forensic sciences, blood is a shred of essential evidence for reconstructing crime scenes. Blood identification and classification may help to confirm a suspect, although several chemical processes are used to recreate the crime scene. However, these approaches can have an impact on DNA analysis. A potential application of bloodstain identification and classification using Hyperspectral Imaging (HSI) can be used as substance clas-sification in forensic science for crime scene analysis. Therefore, this work proposes the use of a fast and compact Hybrid CNN to process HSI data for bloodstain identification and classification. For experimental and validation purposes, we perform exper-iments on a publicly available Hyperspectral-based Bloodstain dataset. This dataset has different types of substances i.e., blood and blood-like compounds, for instance, ketchup, artificial blood, beetroot juice, poster paint, tomato concentrate, acrylic paint, uncertain blood. We compare the results with state-of-the-art 3D CNN model and examine the results in detail and present a discussion of each tested architecture with limited availability of the training samples (e.g., only 5percent (792 samples) of the data samples are used to train the model, and validated on 5percent (792 samples) data samples and finally blindly tested on 90percent (14260 samples) of the data samples). The source code can be access on https://github.com/MHassaanButt/FCHCNN-for-HSIC}, keywords = {Training, Deep learning, Solid modeling, Three-dimensional displays, Predictive models, Data models, Convolutional neural networks, Hyperspectral Imaging, Blood Strain Classification, Fast 3D-CNN, Hybird CNN, Forensic Sciences}, doi = {doi:10.1109/CEC55065.2022.9870277}, notes = {Also known as \cite{9870277}}, ) @INPROCEEDINGS(Cai:2022:CEC, %xplor 24 Sep 2022 author = {Qing Cai and Haojie Ang and Sameer Alam}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Multiobjective Optimization Approach for Air Traffic Flow Management for Airspace Safety Enhancement}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This work aims to enhance the safety of air traffic in a procedural airspace by air traffic flow management (ATFM) without compromizing air traffic demand. Inc ivil aviation, collision risk is an important indicator for air traffic safety assessment. In this work, we propose a generic ATFM framework based on multiobjective optimization for reducing lateral collision risk of the traffic ina given procedural airspace. T he proposed framework aims to optimize the flight level assignment f or a given set of flight plans such that t he lateral collision risk can be reduced. To achieve this goal, we formulate an optimization problem containing two partially conflicting o bjective functions. We then adopt three well-known evolutionary algorithms, i.e., MODPSO, NSGA-II, and MOEA/D to solve the optimization problem. We specially design some of the operators of those al-gorithms to make them suitable for the optimization problem. We merge the solutions yielded by those three algorithms and filter out the final Pareto solutions. We carry o ut a case study o n the procedural airspace of Singapore flight information region (FIR) with respect to twelve daily traffic data selected from t he real traffic data for December 2019. Experiment results demonstrates that the lateral occupancy which is the key contributor to lateral risk can be reduced by 10.65percent to 93.05percent at a strategic planning level. This research contribute to strategic flight planning by assigning flight levels that m ay reduce t he risk o f collision in procedural airspace.}, keywords = {Finite impulse response filters, Atmospheric modeling, Evolutionary computation, Strategic planning, Filtering algorithms, Search problems, Information filters}, doi = {doi:10.1109/CEC55065.2022.9870355}, notes = {Also known as \cite{9870355}}, ) @INPROCEEDINGS(Lyu:2022:CEC, %xplor 24 Sep 2022 author = {Ziyuan Lyu and Tian Ding and Yuwei Fan}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Decomposition-Combination Optimization Method for Network Multiobjective Optimization Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In recent years, an increasing number of multiobjective evolutionary algorithms (MOEAs) have been developed for solving multiobjective optimization problems (MOPs). However, a special type of network-oriented MOPs, termed network multiobjective optimization problems (NMOPs), remains largely unexplored. In this paper, we put forth a general formulation for NMOPs, and propose the network decomposition-combination multiobjective optimization method (NDCMOM) for solving them. NDCMOM adopts a network decomposition mechanism, which breaks the NMOP down to a series of MOPs with smaller scales. The solutions to the NMOP can be obtained by combining the solutions to these smaller-scale MOPs. This decomposition-combination mechanism significantly improves the optimization efficiency. Experiments are conducted to demonstrate that the proposed NDCMOM outperforms the state-of-the-art MOEAs on large-scale NMOPs.}, keywords = {Schedules, Sociology, Optimization methods, Evolutionary computation, Statistics, Evolutionary algorithm, multiobjective optimization, network decomposition, large-scale optimization}, doi = {doi:10.1109/CEC55065.2022.9870404}, notes = {Also known as \cite{9870404}}, ) @INPROCEEDINGS(Hsieh:2022:CEC, %xplor 24 Sep 2022 author = {Cheng-Yu Hsieh and Rung-Tzuo Liaw}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Summit-assisted Evolutionary Multitasking}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Evolutionary computation has served as a blooming research area for decades, in which evolutionary algorithms are inspired from mechanisms of evolution as well as cognitive and social behaviors of creatures in nature as searching processes. An emerging branch of evolutionary computation proposed in recent year is the evolutionary multitasking, which is to manipulate evolutionary algorithms for tackling multitask optimization problems. Recent studies of evolutionary multitasking have drawn much attention on designing effective knowledge transfer mechanisms. This study proposes a novel evolutionary multi-tasking method named summit-assisted evolutionary multitasking (SaEMT) by integrating summit-based knowledge transfer into the general multi-population evolutionary multitasking method. There are two main features in the proposed method, including the summit-based recombination, and the dynamic control of transfer rate. Empirical results show that the proposed method can outperform classical and advanced evolutionary multitasking methods in terms of solution quality and convergence speed. Experimental results also discover that the SaEMT is able to complete in acceptable running time.}, keywords = {Simulation, Evolutionary computation, Multitasking, Behavioral sciences, Task analysis, Knowledge transfer, Optimization, Summit-based knowledge transfer, Evolutionary multitasking, Multitask optimization, Coevolution}, doi = {doi:10.1109/CEC55065.2022.9870205}, notes = {Also known as \cite{9870205}}, ) @INPROCEEDINGS(Wang:2022:CEC, %xplor 24 Sep 2022 author = {Shenqing Wang and Ruifen Cao and Ye Tian and Chunhou Zheng}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Hybrid Tourism Recommendation System: A Multi-Objective Perspective}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={A smart recommendation method can greatly improve tourists' travel experience, and it is an important task for tourism recommendation systems to intelligently suggest scenic spots for tourists according to their historical visit records. Currently, the collaborative filtering and deep neural network-based methods occupy the mainstream of tourism recommendation systems. Although each type of recommendation methods is superior over the others in terms of different aspects, the performance of a single recommendation method is limited. In order to inherit the advantages of different types of recommendation methods, this work suggests a hybrid method for assembling multiple methods for tourism recommendation. Based on the scenic spots obtained by multiple recommendation methods, the proposed hybrid method uses two novel objectives to evaluate each scenic spot, and identifies the best $K$ scenic spots via the techniques used in evolutionary multi-objective optimization. In comparison to existing recommendation methods and hybrid methods, the proposed hybrid method exhibits better performance on two public tourism datasets and a new dataset created based on the tourism information of Huangshan City.}, keywords = {Collaborative filtering, Urban areas, Evolutionary computation, Object recognition, Task analysis, Recommender systems, Optimization}, doi = {doi:10.1109/CEC55065.2022.9870401}, notes = {Also known as \cite{9870401}}, ) @INPROCEEDINGS(De-Moraes:2022:CEC, %xplor 24 Sep 2022 author = {Matheus Bernardelli {De Moraes} and Guilherme Palermo Coelho}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Random Forest-Assisted Decomposition-Based Evolutionary Algorithm for Multi-Objective Combinatorial Optimization Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Many real-world optimization problems involve time-consuming fitness evaluation. To reduce the computational cost of expensive evaluations, researchers have been developing surrogate models to approximate the objective function values of unevaluated candidate solutions. However, most of the research has been developed for continuous optimization problems, while only a few of them address surrogate modeling for expensive multi-objective Combinatorial Optimization Problems (COPs). COPs have inherently different challenges than continuous optimization. For example, (i) many COPs have categorical and nominal decision variables; (ii) they often require the combination of both global and local search mechanisms; and (iii) some of them have constraints that make them NP-hard problems, which makes them even more difficult to solve with a reasonable number of fitness evaluations. To address these issues, this paper proposes a surrogate-assisted evolutionary algorithm that combines the decomposition-based algorithm MOEA/D, Tabu Local Search, and Random Forest as a surrogate model to approximate the objective function of unevaluated individuals on multi-objective COPs. Experiments were conducted on constrained and unconstrained well-known multi-objective combinatorial optimization benchmark problems. The experimental results demonstrate that the proposed design outperforms state-of-the-art algorithms without violating the restrictions in the number of objective function evaluations, which indicates that it may be suitable for real-world expensive multi-objective COPs.}, keywords = {Radio frequency, Analytical models, Computational modeling, Evolutionary computation, Forestry, Benchmark testing, Linear programming, Expensive Multi-Objective Combinatorial Optimization, Multi-Objective Evolutionary Algorithm Based on Decomposition, Random Forest, Surrogate Models, Tabu Search}, doi = {doi:10.1109/CEC55065.2022.9870412}, notes = {Also known as \cite{9870412}}, ) @INPROCEEDINGS(Chen:2022:CEC, %xplor 24 Sep 2022 author = {Weixi Chen and Huachao Dong and Peng Wang and Xiaozuo Liu}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Blended-wing-body underwater glider shape transfer optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The blended-wing-body underwater glider (BWBUG) is a new type of underwater vehicle that has been applied in natural resource exploration with great success. Compared with conventional torpedo shapes, BWBUG's shape has a higher lift-to-drag ratio (LDR), so its shape design has become a research focus of ocean engineering in recent years. It is noteworthy that the traditional design process assumes no editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, prior knowledge and starts from scratch. However, since problems rarely exist in isolation, solving the shape problem of a traditional glider may provide useful information, but the disparity in design space impedes information transmission. This paper presents a heterogeneous transfer optimization method for glider shape, which consists of four parts: simulation, image processing, manifold learning, and the evolution algorithm. The simulation's goal is to create pressure and velocity clouds. Manifold learning will use the information from cloud maps to create a low-dimensional feature space. The information mapped in low-dimensional space will be used to assist evolutionary algorithms in searching for optimal solutions. The proposed method was tested for the shape optimization problem of a BWBUG, and the results show that knowledge learned from different but related problem domains is potentially beneficial to the new design.}, keywords = {Shape, Image processing, Oceans, Optimization methods, Evolutionary computation, Information processing, Manifold learning, transfer optimization, image processing, manifold learning, shape design}, doi = {doi:10.1109/CEC55065.2022.9870267}, notes = {Also known as \cite{9870267}}, ) @INPROCEEDINGS(Mucke:2022:CEC, %xplor 24 Sep 2022 author = {Sascha Mucke and Nico Piatkowski}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Quantum- Inspired Structure- Preserving Probabilistic Inference}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Probabilistic methods serve as the underlying frame-work of various machine learning techniques. When using these models, a central problem is that of computing the partition function, whose computation is intractable for many models of interest. Here, we present the first quantum-inspired method that is especially designed for computing fast approximations to the partition function. Our approach uses a novel hardware solver for quadratic unconstrained binary optimization problems that relies on evolutionary computation. The specialized design allows us to assess millions of candidate solutions per second, leading to high quality maximum a-posterior (MAP) estimates, even for hard instances. We investigate the expected run-time of our solver and devise new ultra-sparse parity constraints to combine our device with the WISH approximation scheme. A SIMD-like packing strategy further allows us to solve multiple MAP instances at once, resulting in high efficiency and an additional speed-up. Numerical experiments show that our quantum-inspired approach produces accurate and robust results. While pure software implementations of the WISH algorithm typically run on large compute clusters with hundreds of CPUs, our results are achieved on two FPGA boards which both consume below 10 Watts. Moreover, our results extend seamlessly to adiabatic quantum computers.}, keywords = {Quantum computing, Computational modeling, Software algorithms, Evolutionary computation, Machine learning, Probabilistic logic, Hardware, probabilistic inference, quantum annealing, fpga, evolutionary computation}, doi = {doi:10.1109/CEC55065.2022.9870260}, notes = {Also known as \cite{9870260}}, ) @INPROCEEDINGS(Masood:2022:CEC, %xplor 24 Sep 2022 author = {Atiya Masood and Gang Chen and Yi Mei and Harith Al-Sahaf and Mengjie Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic Programming Hyper-heuristic with Gaussian Process-based Reference Point Adaption for Many-Objective Job Shop Scheduling}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Job Shop Scheduling (JSS) is an important real-world problem. However, the problem is challenging because of many conflicting objectives and the complexity of production flows. Genetic programming-based hyper-heuristic (GP-HH) is a useful approach for automatically evolving effective dispatching rules for many-objective JSS. However, the evolved Pareto-front is highly irregular, seriously affecting the effectiveness of GP-HH. Although the reference points method is one of the most prominent and efficient methods for diversity maintenance in many-objective problems, it usually uses a uniform distribution of reference points which is only appropriate for a regular Pareto-front. In fact, some reference points may never be linked to any Pareto-optimal solutions, rendering them useless. These useless reference points can significantly impact the performance of any reference-point-based many-objective optimization algorithms such as NSGA-III. This paper proposes a new reference point adaption process that explicitly constructs the distribution model using Gaussian process to effectively reduce the number of useless reference points to a low level, enabling a close match between reference points and the distribution of Pareto-optimal solutions. We incorporate this mechanism into NSGA-III to build a new algorithm called MARP-NSGA-III which is compared experimentally to several popular many-objective algorithms. Experiment results on a large collection of many-objective benchmark JSS instances clearly show that MARP-NSGA-III can significantly improve the performance by using our Gaussian Process-based reference point adaptation mechanism.}, keywords = {Adaptation models, Job shop scheduling, Processor scheduling, Sociology, Gaussian processes, Production, Maintenance engineering, Many-objective Optimization, Evolutionary Computation, Gaussian Process, Genetic Programming, Adaptive reference points, Job Shop Scheduling}, doi = {doi:10.1109/CEC55065.2022.9870322}, notes = {Also known as \cite{9870322}}, ) @INPROCEEDINGS(Bisneto:2022:CEC, %xplor 24 Sep 2022 author = {Elpidio C. De A. Bisneto and Mariana B.S. A. {De Brito} and Sebastien RMJ Rondineau and Daniel M. Munoz}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Comparison of evolutionary algorithms for synthesis of linear array of antennas with minimal level of sidelobe}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={With the great increase of IoT devices available, new techniques seek to accommodate them into the networks. For antenna arrays applied to Simultaneous Wireless Information and Power Transfer (SWIPT), it is necessary to guarantee the minimum amount of power loss possible. For this purpose, the analysis of techniques that decrease the side lobe levels of the antenna radiation patterns are of great interest. Traditional methods tend to only be applied to arrays of equally spaced antennas. One possible way to overcome this obstacle is to apply bioinspired algorithms. The present work compares the particle swarm optimization (PSO) and differential evolution (DE) bioinspired algorithms and its opposition-based learning (OBL) variation to Kaiser, Dolph-Chebyshev and uniform windows, classically used for the improvement of transmitted power. The analysis reached SLL values of -54.92 dB and -44.16 dB for an array of 10 and 16 elements respectively. According to the statistical analysis, OBL provides the best results. OPSO and ODE were the most robust algorithms and OPSO performs better for a 16 elements array, while ODE performs better for a 10 elements array.}, keywords = {Statistical analysis, Linear antenna arrays, Internet of Things, Particle swarm optimization, Simultaneous wireless information and power transfer, Antenna radiation patterns, Array Antennas, Evolutionary Computation, Particle Swarm Optimization, Differential Evolution, Opposition-based learning}, doi = {doi:10.1109/CEC55065.2022.9870362}, notes = {Also known as \cite{9870362}}, ) @INPROCEEDINGS(Khosrowshahli:2022:CEC, %xplor 24 Sep 2022 author = {Rasa Khosrowshahli and Shahryar Rahnamayan and Azam Asilian Bidgoli}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Clustering Center-based Differential Evolution}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of the center-based sampling can be utilized at either or both the operation and/or population level. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at operation level requires customizing the strategy for a specific algorithm which degrades the scheme's generalization. In this paper, we have proposed a population-level center-based sampling method which is operation independent and correspondingly can be embedded in any population-based optimization algorithm. In this study, we applied the proposed scheme for Differential Evolution (DE) algorithm to enhance the exploration and exploitation capabilities of the algorithm. We cluster candidate solutions and inject the centroid-based samples into the population to increase the overall quality of the population and thus decrease the risk of premature convergence and stagnation. By a high chance, the center-based samples are effectively generated in the promising regions of the search space. The proposed method has been benchmarked by employing CEC-2017 benchmark test suite on dimensions 30, 50, and 100. The results clearly indicate the superiority of the proposed scheme, and a detailed results analysis is provided.}, keywords = {Sociology, Metaheuristics, Clustering algorithms, Evolutionary computation, Benchmark testing, Sampling methods, Convergence, Evolutionary Algorithms, Differential Evolution, Center-based Sampling, Optimization, Population-based Algorithms, Clustering}, doi = {doi:10.1109/CEC55065.2022.9870429}, notes = {Also known as \cite{9870429}}, ) @INPROCEEDINGS(Hamilton:2022:CEC, %xplor 24 Sep 2022 author = {Nolan H. Hamilton and Errin W. Fulp}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Budgeted Classification with Rejection: An Evolutionary Method with Multiple Objectives}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Classification systems are often deployed in resource-constrained settings where labels must be assigned to inputs on a budget of time, memory, etc. Budgeted, sequential classifiers (BSCs) address these scenarios by processing inputs through a sequence of partial feature acquisition and evaluation steps with early-exit options. This allows for an efficient evaluation of inputs that prevents unneeded feature acquisition. To approximate an intractable combinatorial problem, current approaches to budgeted classification rely on well-behaved loss functions that account for two primary objectives (processing cost and error). These approaches offer improved efficiency over traditional classifiers but are limited by analytic constraints in formulation and do not manage additional performance objectives. Notably, such methods do not explicitly account for an important aspect of real-time detection systems--the fraction of "accepted" predictions satisfying a confidence criterion imposed by a risk-averse monitor. We propose a problem-specific genetic algorithm to build budgeted, sequential classifiers with confidence-based reject options. Three objectives--accuracy, processing time/cost, and coverage--are considered. The algorithm emphasizes Pareto efficiency while accounting for a notion of aggregate performance via a unique scalarization. Experiments show our method can quickly find globally Pareto optimal solutions in very large search spaces and is competitive with existing approaches while offering advantages for selective, budgeted deployment scenarios.}, keywords = {Costs, Protocols, Evolutionary computation, Pareto optimization, Prediction algorithms, Feature extraction, Real-time systems, machine learning, budgeted classification, reject option, early-exit, selective classification, evolutionary computation}, doi = {doi:10.1109/CEC55065.2022.9870382}, notes = {Also known as \cite{9870382}}, ) @INPROCEEDINGS(da-Silva:2022:CEC, %xplor 24 Sep 2022 author = {Felipe Rooke {da Silva} and Alex Borges Vieira and Heder Soares Bernardino and Victor Aquiles Alencar and Lucas Ribeiro Pessamilio and Helio Jose Correa Barbosa}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Automated Machine Learning for Time Series Prediction}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Automated Machine Learn (AutoML) process is target of large studies, both from academia and industry. AutoML reduces the demand for data scientists and makes specialists in specific fields able to use Machine Learn (ML) in their domains. An application of ML algorithms is over time-series forecasting, and about these, few works involve the application of AutoML. In this work, an AutoML approach that aggregates time-series forecasting models is proposed. Furthermore, a special focus is given to the optimization stage, which uses genetic algorithm to boost searching for hyper-parameters. In the end, results are compared with a recent time-series forecasting benchmark and we verify that the AutoML model proposed in this work surpasses the benchmark.}, keywords = {Aggregates, Time series analysis, Metaheuristics, Optimization methods, Benchmark testing, Predictive models, Prediction algorithms, automl, data science, time series, forecasting}, doi = {doi:10.1109/CEC55065.2022.9870305}, notes = {Also known as \cite{9870305}}, ) @INPROCEEDINGS(Ramadas:2022:CEC, %xplor 24 Sep 2022 author = {Meera Ramadas and Ajith Abraham}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Segregating Satellite Imagery Based on Soil Moisture Level Using Advanced Differential Evolutionary Multilevel Segmentation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Soil Moisture aid analysts in study of soil science, agriculture and hydrology. Satellite imagery for soil moisture estimation is recorded through earth satellites. By segmenting these satellite imageries based on soil moisture content, we can effortlessly identify regions of wetter condition and regions of dry condition. Differential evolution (DE) is a popular evolutionary approach that is used to optimize problems like image segmentation. In this work, an Advanced Differential Evolution (aDE) technique is introduced which has enhanced performance in comparison to traditional DE approach. This approach is combined with Renyi's entropy for performing multilevel segmentation on the imagery. The resultant segmented images obtained on using the proposed technique is of enhanced quality.}, keywords = {Image segmentation, Hydrology, Satellites, Statistical analysis, Soil moisture, Weather forecasting, Moisture, Renyi's entropy, soil moisture content, PSNR, segmentation}, doi = {doi:10.1109/CEC55065.2022.9870422}, notes = {Also known as \cite{9870422}}, ) @INPROCEEDINGS(Nguyen:2022:CEC, %xplor 24 Sep 2022 author = {Hieu Trung Nguyen and Khang Tran and Ngoc Hoang Luong}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Combining Soft-Actor Critic with Cross-Entropy Method for Policy Search in Continuous Control}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this paper, we propose CEM-SAC - a hybridization between the cross-entropy method (CEM), i.e., an estimation-of-distribution algorithm, and the soft-actor critic (SAC), i.e., a state-of-the-art policy gradient algorithm. Our work extends the evolutionary reinforcement learning (ERL) line of research on integrating the robustness of population-based stochastic black-box optimization, that typically assumes little to no problem-specific knowledge, into the training process of policy gradient algorithms, that exploits the sequential decision making nature for efficient gradient estimation. Our hybrid approach, CEM-SAC, exhibits both the stability of CEM and the efficiency of SAC in training policy neural networks of reinforcement learning agents for solving control problems. Experimental result comparisons with the three baselines CEM, SAC, and CEM-TD3, a recently-introduced ERL method that combines CEM and the twin-delayed deep deterministic policy gradient (TD3) algorithm, on a wide range of control tasks in the MuJoCo benchmarks confirm the enhanced performance of our proposed CEM-SAC. The source code is available at https://github.com/ELO-Lab/CEM-SAC.}, keywords = {Training, Sociology, Neural networks, Process control, Reinforcement learning, Stability analysis, Robustness, Reinforcement learning, Evolutionary computation, Cross-entropy method, Soft actor-critic, Policy search}, doi = {doi:10.1109/CEC55065.2022.9870209}, notes = {Also known as \cite{9870209}}, ) @INPROCEEDINGS(Takubo:2022:CEC, %xplor 24 Sep 2022 author = {Yuji Takubo and Masahiro Kanazaki}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Robust Constrained Multi-objective Evolutionary Algorithm based on Polynomial Chaos Expansion for Trajectory Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={An integrated optimization method based on the constrained multi-objective evolutionary algorithm (MOEA) and non-intrusive polynomial chaos expansion (PCE) is proposed, which solves robust multi-objective optimization problems under time-series dynamics. The constraints in such problems are difficult to handle, not only because the number of the dynamic constraints is multiplied by the discretized time steps but also because each of them is probabilistic. The proposed method rewrites a robust formulation into a deterministic problem via the PCE, and then sequentially processes the generated constraints in population generation, trajectory generation, and evaluation by the MOEA. As a case study, the landing trajectory design of supersonic transport (SST) with wind uncertainty is optimized. Results demonstrate the quantitative influence of the constraint values over the optimized solution sets and corresponding trajectories, proposing robust flight controls.}, keywords = {Chaos, Space vehicles, Uncertainty, Wind speed, Optimization methods, Optimal control, Evolutionary computation, Robust Optimization, Multi-objective Optimization, Evolutionary Algorithm, Polynomial Chaos Expansion, Trajectory Optimization}, doi = {doi:10.1109/CEC55065.2022.9870365}, notes = {Also known as \cite{9870365}}, ) @INPROCEEDINGS(da-Silva:2022:CEC, %xplor 24 Sep 2022 author = {Juarez M. {da Silva} and Gabriel {de O. Ramos} and Jorge L. V. Barbosa}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={The multi-objective dynamic shortest path problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Multi-objective decision-making and dynamic short-est paths are two areas of research widely studied and of great importance for computer science, engineering, and economics. Both areas have seen a remarkable evolution in their algorithms in the last decades and have contributed to several applications in real life. The applications range from cost reduction in the expansion of telecommunications and electrical networks to the accessibility of people and autonomous vehicles. However, despite their importance, the investigation of methods at the intersection of these two areas has not been explored in the literature. Problems at this intersection are characterized by graphs whose topology can change over time (i.e., edges can be inserted or deleted online) and whose edges' costs are defined by more than a single criterion or objective. In this paper, we introduce the multi- objective dynamic shortest path problem (MODSP) and present the first algorithm to solve it. In particular, we formally define the MODSP problem and explain its relation to multi-objective decision-making and dynamic shortest paths. Concerning the algorithm, we present the first single-source MODSP (SMDSP) approach as a first effort towards solving MODSP problems while avoiding the recomputation of the paths from scratch when the graph is updated. Finally, we perform an experimental evaluation of the SMDSP and a comparison with the state-of-art algorithm for multi-objective shortest path problems. The results of our experimental evaluation prove that in an environment subjected to constant updates, the SMDSP algorithm is more efficient.}, keywords = {Shortest path problem, Costs, Network topology, Heuristic algorithms, Decision making, Evolutionary computation, Topology, Multi-Objective, decision-making, Dynamic Shortest Path}, doi = {doi:10.1109/CEC55065.2022.9870278}, notes = {Also known as \cite{9870278}}, ) @INPROCEEDINGS(Leonard:2022:CEC, %xplor 24 Sep 2022 author = {Brydon A. Leonard and Mathys C. {Du Plessis}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Bootstrapped Neuro-Simulation for Damage Recovery in Complex Robots}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Bootstrapped Neuro-Simulation is a technique in which a Neural Network Simulator, used for the evaluation of controllers during the Evolutionary Robotics process, is trained concurrently with the evolution of the controllers themselves. This removes the need for creating a simulator before commencing controller evolution and results in a simulator that is not only tailored specifically to the robot being used, but also to the task that the robot is expected to perform. This paper demonstrates that Bootstrapped Neuro-Simulation can also be used for damage recovery since the Neural Network Simulator adapts to physical changes to the robot and enables the evolution of controllers that utilize the undamaged components of the robot. Limbs of a hexapod robot are disabled to simulate damage in the experiments described in this paper. Various adaptations to the Bootstrapped Neuro-Simulation algorithm are investigated in simulation. A real-world robot is used to demonstrate the successful recovery from damage and to illustrate situations where the adaptations were found to be beneficial.}, keywords = {Legged locomotion, Training, Evolutionary robotics, Adaptation models, Neural networks, Training data, Process control, evolutionary robotics, damage recovery, neuro-simulation}, doi = {doi:10.1109/CEC55065.2022.9870204}, notes = {Also known as \cite{9870204}}, ) @INPROCEEDINGS(Rosenberg:2022:CEC, %xplor 24 Sep 2022 author = {Manou Rosenberg and Mark Reynolds and Tim French and Lyndon While}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Algorithms for Planning Remote Electricity Distribution Networks Considering Isolated Microgrids and Geographical Constraints}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this study we propose obstacle-aware evolution-ary algorithms to identify optimised network topologies for electricity distribution networks including isolated microgrids or stand-alone power systems. We outline the extension of two evo-lutionary algorithms that are modified to consider different types of geographically constrained areas in electricity distribution planning. These areas are represented as polygonal obstacles that either cannot be traversed or cause a higher weight factor when traversing. Both proposed evolutionary algorithms are extended such that they find optimised network solutions that avoid solid obstacles and consider the increased cost of traversing soft obstacles. The algorithms are tested and compared on different types of problem instances with solid and soft obstacles and the problem-specific evolutionary algorithm can be shown to successfully find low cost network topologies on a range of different test instances.}, keywords = {Costs, Network topology, Evolutionary computation, Microgrids, Distribution networks, Solids, Planning, Electricity network planning with obstacles, evo-lutionary algorithms, polygonal constraints, distributed network topology, isolated microgrids}, doi = {doi:10.1109/CEC55065.2022.9870233}, notes = {Also known as \cite{9870233}}, ) @INPROCEEDINGS(Franken:2022:CEC, %xplor 24 Sep 2022 author = {Lukas Franken and Bogdan Georgiev and Sascha Mucke and Moritz Wolter and Raoul Heese and Christian Bauckhage and Nico Piatkowski}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Quantum Circuit Evolution on {NISQ} Devices}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Variational quantum circuits build the foundation for various classes of quantum algorithms. In a nutshell, the weights of a parametrized quantum circuit are varied until the empirical sampling distribution of the circuit is sufficiently close to a desired outcome. Numerical first-order methods are applied frequently to fit the parameters of the circuit, but most of the time, the circuit itself, that is, the actual composition of gates, is fixed. Methods for optimizing the circuit design jointly with the weights have been proposed, but empirical results are rather scarce. Here, we consider a simple evolutionary strategy that addresses the trade-off between finding appropriate circuit ar-chitectures and parameter tuning. We evaluate our method both via simulation and on actual quantum hardware. Our benchmark problems include the transverse field Ising Hamiltonian and the Sherrington-Kirkpatrick spin model. Despite the shortcomings of current noisy intermediate-scale quantum hardware, we find only a minor slowdown on actual quantum machines compared to simulations. Moreover, we investigate which mutation operations most significantly contribute to the optimization. The results provide intuition on how randomized search heuristics behave on actual quantum hardware and layout a path for further refinement of evolutionary quantum gate circuits.}, keywords = {Performance evaluation, Quantum algorithm, Stationary state, Logic gates, Hardware, Circuit synthesis, Integrated circuit modeling, variational quantum circuits, structure learning, evolutionary computation}, doi = {doi:10.1109/CEC55065.2022.9870269}, notes = {Also known as \cite{9870269}}, ) @INPROCEEDINGS(Filippi:2022:CEC, %xplor 24 Sep 2022 author = {Gianluca Filippi and Massimiliano Vasile and Edoardo Patelli and Marco Fossati}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Generative Optimisation of Resilient Drone Logistic Networks}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper presents a novel approach to the gener-ative design optimisation of a resilient Drone Logistic Network (DLN) for the delivery of medical equipment in Scotland. A DLN is a complex system composed of a high number of different classes of drones and ground infrastructures. The corresponding DLN model is composed of a number of interconnected digital twins of each one of these infrastructures and vehicles, forming a single digital twin of the whole logistic network. The paper proposes a multi-agent bio-inspired optimisation approach based on the analogy with the Physarum Policefalum slime mould that incrementally generates and optimise the DLN. A graph theory methodology is also employed to evaluate the network resilience where random failures, and their cascade effect, are simulated. The different conflicting objectives are aggregated into a single global performance index by using Pascoletti-Serafini scalarisation.}, keywords = {Evolutionary computation, Routing, Graph theory, Digital twins, Performance analysis, Complex systems, Optimization, Physarum Optimisation, Digital Twin, Drone Logistic Network, Vehicle Routing Problem, Complex System, Graph Theory, Resilience}, doi = {doi:10.1109/CEC55065.2022.9870306}, notes = {Also known as \cite{9870306}}, ) @INPROCEEDINGS(Chiang:2022:CEC, %xplor 24 Sep 2022 author = {Tsung-Che Chiang and Thammarsat Visutarrom and Sadan Kulturel-Konak and Abdullah Konak}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An Adaptive Multiobjective Evolutionary Algorithm for Economic Emission Dispatch}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper addresses the economic emission dispatch (EED) problem where the goal is to allocate the power output of power generation units to satisfy power demand and minimize the cost and emissions simultaneously. We propose a multiobjective differential evolution algorithm and a reinforcement learning technique to adaptively control the parameters of differential evolution. Moreover, the proposed approach utilizes mating restriction and preferences in mating selection to improve search effectiveness and a dynamically controlled mutation to increase the exploration ability. The proposed ideas and algorithm were examined using four EED test cases. Experimental results showed positive effects of our proposed methods and the competitive performance of our algorithm.}, keywords = {Power demand, Costs, Heuristic algorithms, Evolutionary computation, Reinforcement learning, Power generation, economic dispatch, emission, multiobjective, evolutionary algorithm, adaptive control, reinforcement learning, parameter control}, doi = {doi:10.1109/CEC55065.2022.9870330}, notes = {Also known as \cite{9870330}}, ) @INPROCEEDINGS(Shi:2022:CEC, %xplor 24 Sep 2022 author = {Gaofeng Shi and Fangfang Zhang and Yi Mei}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Novel Fitness Function for Genetic Programming in Dynamic Flexible Job Shop Scheduling}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Dynamic flexible job shop scheduling (DF JSS) is a complex and challenging combinatorial optimisation problem. In DF JSS, job operations have to be processed on a set of machines, and thus machine assignment and operation sequencing decisions need to be made simultaneously in dynamic situations. Genetic programming (GP), as a hyper-heuristic approach, has been widely used to learn scheduling heuristics for DF JSS automati-cally. However, the traditional GP parent selection method based on fitness value only may not be sufficiently effective, since not all the subtrees of a GP individual are meaningful and can contribute to the goodness of the individual. This paper proposes a new GP algorithm with a novel fitness function by incorporating the subtree importance into the parent selection method. Specifically, the subtree importance is measured by the correlation coefficient between the behaviour of subtrees and the GP individual. The proposed algorithm is expected to improve the effectiveness of GP by capturing more useful subtrees for producing offspring to the next generation. This paper uses nine DF JSS scenarios to examine the effectiveness of the proposed algorithm. The results show that the proposed algorithm achieves slightly better performance in some of the scenarios while no worse in all other scenarios. Further analyses, including the effect of the designed fitness function and sizes of the learned scheduling heuristics, are also conducted.}, keywords = {Sequential analysis, Job shop scheduling, Processor scheduling, Heuristic algorithms, Genetic programming, Evolutionary computation, Dynamic scheduling, Parent Selection, Fitness Function, Genetic Pro-gramming, Dynamic Flexible Job Shop Scheduling}, doi = {doi:10.1109/CEC55065.2022.9870235}, notes = {Also known as \cite{9870235}}, ) @INPROCEEDINGS(Ali:2022:CEC, %xplor 24 Sep 2022 author = {Ismail M. Ali and Hasan Huseyin Turan and Sondoss Elsawah}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Discrete Differential Evolution Algorithm for a Military Fleet Modernization Problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Differential evolution has a long track record of successfully solving optimization problems in continuous domain due it its powerful Euclidean distance-based learning concept. Although this affects its suitability for solving several problems with permutation variables, several studies show that it can be applicable for effectively solving permutation-based problems. In this paper, an improved design of differential evolution is introduced to solve a military fleet modernization problem with discrete parameters. In this problem, several modernization oper-ations are required to transition a military force from an outdated fleet to a more modern one with the objective of maximizing the force's deployment at the minimum cost over a pre-determined planning period. The proposed differential evolution incorporates a new solution representation, a proposed repairing heuristic method, a modified mutation operator and mapping method for efficiently tackling the discrete characteristics of the targeted problem and is coupled with a simulation model to evaluate the fitness of the generated solutions. To judge its performance, the proposed algorithm has been implemented to solve a case study that addresses recent fleet modernization strategies of the Australian Army to recapitalize its forces over the next decade and in a continual process. The experimental results show that the proposed algorithm can provide more efficient fleet modernization schedules which are 29.3percent and 51.4percent better than those obtained by other two comparative algorithms.}, keywords = {Schedules, Costs, Processor scheduling, Military computing, Force, Evolutionary computation, Scheduling, Differential evolution, military fleet modernization scheduling problem, combinatorial optimization problem}, doi = {doi:10.1109/CEC55065.2022.9870320}, notes = {Also known as \cite{9870320}}, ) @INPROCEEDINGS(Andersen:2022:CEC, %xplor 24 Sep 2022 author = {Hayden Andersen and Andrew Lensen and Will N. Browne and Yi Mei}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving Counterfactual Explanations with Particle Swarm Optimization and Differential Evolution}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Counterfactual explanations are a popular eXplainable AI technique, used to provide contrastive answers to "what-if" questions. These explanations are consistent with the way that an everyday person will explain an event, and have been shown to satisfy the 'right to explanation' of the European data regulations. Despite this, current work to generate counterfactual explanations either makes assumptions about the model being explained or utlises algorithms that perform suboptimally on continuous data. This work presents two novel algorithms to generate counterfactual explanations using Particle Swarm Optimization (PSO) and Differential Evolution (DE). These are shown to provide effective post-hoc explanations that make no assumptions about the underlying model or data structure. In particular, PSO is shown to generate counterfactual explanations that utilise significantly fewer features to generate sparser explanations when compared to previous related work.}, keywords = {Sociology, Europe, Production, Data structures, Data models, Regulation, Particle swarm optimization, explainable AI, counterfactual explanation, particle swarm optimization, differential evolution}, doi = {doi:10.1109/CEC55065.2022.9870283}, notes = {Also known as \cite{9870283}}, ) @INPROCEEDINGS(de-Andrade-Amorim-Neto:2022:CEC, %xplor 24 Sep 2022 author = {Hugo {de Andrade Amorim Neto} and Marcelo Gomes Pereira {de Lacerda} and Fernando Buarque {de Lima Neto}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Step-Size Individualization: a Case Study for The Fish School Search Family}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This study proposes a new strategy to improve the performance of the algorithms of the Fish School Search (FSS) family via the individualization of the step-size of each fish. We propose to be calculated in two different manners: using individual weight or using individual fitness, depending on the chosen variation of the proposed technique. Our methods were tested on the original FSS, on the Weight based Fish School Search (wFSS) and on the Multi Objective Fish School Search (MOFSS) algorithms. The benchmark functions of the Congress on Evolutionary Computation, The Genetic and Evolutionary Computation Conference (CEC'2020, CEC'2013, and GECCO'2016) and the DTLZ test suite were used to assess the experimental results, which yielded that all variants of the FSS algorithm tested have been improved in the majority of the scenarios.}, keywords = {Evolutionary computation, Benchmark testing, Fish, Search problems, Genetics, Metaheuristic, Population-based algorithms, self-adapting parameters, Fish School Search}, doi = {doi:10.1109/CEC55065.2022.9870212}, notes = {Also known as \cite{9870212}}, ) @INPROCEEDINGS(Wang:2022:CEC, %xplor 24 Sep 2022 author = {Bing Wang and Hemant Kumar Singh and Tapabrata Ray}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Investigating Neighborhood Solution Transfer Schemes for Bilevel Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Bilevel optimization refers to a challenging class of problems where a lower level (LL) optimization task acts as a constraint for an upper level (UL) optimization task. When a bilevel problem is solved using a nested evolutionary algorithm (EA), a large number of function evaluations are consumed since an LL optimization needs to be conducted to evaluate every candidate UL solution. Knowledge transfer of optimal LL solutions between neighboring UL solutions is a plausible approach to improve the search efficiency. Even though some of the past studies have utilized this strategy intuitively, the specific impact of the transferred solution(s) has not been clearly differentiated since it forms only a small component of a much more elaborate search framework. In this study, we intend to examine closely the effectiveness of direct solution transfer. To do so, the transferred solution (LL optimum of the nearest UL solution) is considered as the mainstay of the LL search, acting as the starting point for a direct local LL search. We first observe the performance of this approach on existing benchmarks. Based on the understanding gained from the experiments, we design modified problems where such a direct transfer is likely to face significant challenges. We then propose an improved approach that uses solution transfer more selectively by considering correlations between neighboring landscapes for a more effective transfer. Numerical experiments are conducted to demonstrate the challenges faced by the direct transfer on the modified problems, as well as the competitive performance of the correlation-based approach. We hope that the insights gained from the study will be beneficial for future development of efficient transfer-based approaches for bilevel optimization.}, keywords = {Correlation, Evolutionary computation, Benchmark testing, Task analysis, Optimization, Knowledge transfer, Faces, Bilevel optimization, Transfer learning, Evolutionary algorithms}, doi = {doi:10.1109/CEC55065.2022.9870350}, notes = {Also known as \cite{9870350}}, ) @INPROCEEDINGS(Rodrigues-Neto:2022:CEC, %xplor 24 Sep 2022 author = {Joao B. {Rodrigues Neto} and Gabriel De O. Ramos}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An Interpolated Approach for Active Debris Removal}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The continuous use of satellite networks in the Low Earth Orbit (LEO) has accumulated a large amount of space debris. Given the actual state of the orbit, these debris are a threat to the active systems and to the feasibility of future operations in LEO. Now, Active Debris Removal (ADR) missions must be conducted to mitigate the debris through forced deorbitation. The best documented approaches for the ADR mission planning made use of metaheuristics, modeling the ADR as a complex variant of the TSP. However, these approaches usually fail to deal some of the ADR problem dynamics, such as large instances, mission constraints or the debris motion. In this paper we propose heuristic of continuous improvement on a genetic-based solution. Our work advances the state of the art by dealing with large real world instances, modeling all the constraints and considering the problem time dependence (motion). Experiments were conducted to evidence the improvements over the literature. With the ability of generating time-dependent results for scenarios with thousands of debris in a feasible time, our approach yielded missions 96.33percent more effective at the cleaning job than the present ones on the literature.}, keywords = {Space vehicles, Satellites, Space missions, Space debris, Sociology, Low earth orbit satellites, Mathematical models, Time-Dependent, Active Debris Removal, Traveling Salesman Problem, Genetic Algorithm}, doi = {doi:10.1109/CEC55065.2022.9870437}, notes = {Also known as \cite{9870437}}, ) @INPROCEEDINGS(Ray:2022:CEC, %xplor 24 Sep 2022 author = {Tapabrata Ray and Mohammad Mohiuddin Mamun and Hemant Kumar Singh}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Simple Evolutionary Algorithm for Multi-modal Multi-objective Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In solving multi-modal, multi-objective optimization problems (MMOPs), the objective is not only to find a good representation of the Pareto-optimal front (PF) in the objective space but also to find all equivalent Pareto-optimal subsets (PSS) in the variable space. Such problems are practically relevant when a decision maker (DM) is interested in identifying alternative designs with similar performance. There has been significant research interest in recent years to develop efficient algorithms to deal with MMOPs. However, the existing algorithms still require prohibitive number of function evaluations (often in several thousands) to deal with problems involving as low as two objectives and two variables. The algorithms are typically embedded with sophisticated, customized mechanisms that require additional parameters to manage the diversity and convergence in the variable and the objective spaces. In this paper, we introduce a steady-state evolutionary algorithm for solving MMOPs, with a simple design and no additional user-defined parameters that need tuning compared to a standard EA. We report its performance on 21 MMOPs from various test suites that are widely used for benchmarking using a low computational budget of 1000 function evaluations. The performance of the proposed algorithm is compared with six state-of-the-art algorithms (MO_Ring_PSO_SCD, DN-NSGAII, TriMOEA-TA&R, CPDEA, MMOEA/DC and MMEA-WI). The proposed algorithm exhibits significantly better performance than the above algorithms based on the established metrics including IGDX, PSP and IGD. We hope this study would encourage design of simple, efficient and generalized algorithms to improve their uptake for practical applications.}, keywords = {Measurement, Sociology, Clustering algorithms, Evolutionary computation, Steady-state, Indexes, Statistics, Multiobjective optimization, multimodal opti-mization, evolutionary algorithm}, doi = {doi:10.1109/CEC55065.2022.9870274}, notes = {Also known as \cite{9870274}}, ) @INPROCEEDINGS(Japa:2022:CEC, %xplor 24 Sep 2022 author = {Luis Japa and Marcello Serqueira and Israel Mendonca and Eduardo Bezerra and Masayoshi Aritsugi and Pedro Henrique Gonzalez}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A conjugated evolutionary algorithm for hyperparameter optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={With the recent upsurge in the use of deep learning and other computationally expensive machine learning models, hyperparameter optimization has become a quite important and widely researched area of study. Genetic algorithms, a subclass of evolutionary algorithms, have proven to be an effective approach and have been widely used in recent years. However, efficiently exploring the domain of possible solutions editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, remains a challenging, and often computationally-expensive task. In this paper, we present a novel and efficient hyperparameter optimization strategy based on a genetic algorithms variant: Biased Random-key Genetic Algorithms (BRKGA). One of the main challenges of BRKGA is its limited capacity to explore the domain surrounding a particular individual. Although good genes will be preserved by its bias property, these genes are copied as they are, and even if a better solution exists in the close neighborhood of a particular gene it might never be explored. We tackle this problem by adding an exploitation component at the end of every evolutionary step, further exploring the hyperparameter domain. Several computational experiments on eight different publicly available datasets were performed to assess the effectiveness of the proposed approach and to prove it is a significant improvement over its predecessor. The results show that our proposed method outperforms, in terms of the $F_{1}$ score of the resulting Artificial Neural Network, not only BRKGA but also other commonly used methods in most of the test cases.}, keywords = {Deep learning, Computational modeling, Evolutionary computation, Artificial neural networks, Computational efficiency, Task analysis, Optimization, optimization, hyperparameter optimization, genetic algorithms, evolutionary algorithms, metaheuristic}, doi = {doi:10.1109/CEC55065.2022.9870442}, notes = {Also known as \cite{9870442}}, ) @INPROCEEDINGS(Li:2022:CEC, %xplor 24 Sep 2022 author = {Bin Li and Zhi-Bin Tang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Double-Assimilation of Prosperity and Destruction Oriented Improved Imperialist Competitive Algorithm with Computational Thinking}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Whereas the imperialist competitive algorithm (ICA) shows limited global search ability and be liable to be trapped into local optimum, a double-assimilation of prosperity and destruction oriented improved imperialist competitive algorithm (DPDO-IIC A) is proposed tentatively to overcome inherent defects. The imperialist assimilation and colonial reform strategy are customized purposefully, and a novel population redistribution mechanism is introduced as well. The three improvement measures are supposed to further promote population diversity and searching accuracy. The CEC2017 test set is selected to verify the performance of the DPDO-IICA by the different types of numerical function problems with the different dimensions. Moreover, the DPDO-IICA is compared with the three first-class intelligent optimization algorithms, which have achieved significant rankings in the CEC2017 competition. The comparison shows that the DPDO-IICA has good performances, which is demonstrated by the accuracy and stability. In addition, the proportion of imperialists and colonies is investigated, and it is through the community partitioning and clustering dynamically to enhance the population diversity. In conclusion, the DPDO-IICA can effectively improve the ability of global exploration and avoid premature convergence in comparison with the original ICA.}, keywords = {Heuristic algorithms, Sociology, Clustering algorithms, Robustness, Partitioning algorithms, Statistics, Particle swarm optimization, imperialist competitive algorithm, computational thinking, double assimilation, prosperity and destruction of empires, population redistribution, elite opposition-based learning, differential evolution operator, partitioning and clustering}, doi = {doi:10.1109/CEC55065.2022.9870296}, notes = {Also known as \cite{9870296}}, ) @INPROCEEDINGS(Hu:2022:CEC, %xplor 24 Sep 2022 author = {Caie Hu and Sanyou Zeng and Changhe Li}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Hyperparameters Adaptive Sharing Based on Transfer Learning for Scalable GPs}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Gaussian processes (GPs) are a kind of non-parametric Bayesian approach. They are widely used as surrogate models in data-driven optimization to approximate the exact functions. However, the cubic computation complexity is involved in building GPs. This paper proposes hyperparameters adaptive sharing based on transfer learning for scalable GPs to address the limitation. In this method, the hyperparameters across source tasks are adaptively shared to the target task by the linear predictor. This method can reduce the computation cost of building GPs without losing capability based on experimental analyses. The method's effectiveness is demonstrated on a set of benchmark problems.}, keywords = {Costs, Computational modeling, Transfer learning, Buildings, Gaussian processes, Evolutionary computation, Benchmark testing, Data-driven optimization, Surrogate models, Bayesian, Gaussian process, Transfer learning}, doi = {doi:10.1109/CEC55065.2022.9870288}, notes = {Also known as \cite{9870288}}, ) @INPROCEEDINGS(Singh:2022:CEC, %xplor 24 Sep 2022 author = {Emilio Singh and Nelishia Pillay}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Study of Transfer Learning in an Ant-Based Generation Construction Hyper-Heuristic}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Generation construction hyper-heuristics have proven to be effective in solving discrete optimization problems. Previous work has shown the effectiveness of an ant colony optimization hyper-heuristic for solving scheduling and packing problems. One of the challenges with generation construction hyper-heuristics is the high processing times associated with creating new construction heuristics. While there has been research into using transfer learning to reduce the computational cost of genetic programming generation constructive hyper-heuristics, this has not been investigated for ant colony optimization generation construction hyper-heuristics. In fact to the knowledge of the authors transfer learning has not previously been investigated for ant colony optimization. In this study the knowledge transferred is the pheromone map. The maps are transferred from the source domain to the target domain, with the target domain being more complicated problem instances and the source domain simpler problem instances, which do not take as long to solve. The approach was evaluated on the movie scene scheduling problem, the one dimensional bin packing problem and the quadratic assignment problem. The study has shown that the use of transfer learning has reduced the computational cost drastically while maintaining the same performance for the more complex problems for the movie scene scheduling problem and the quadratic assignment problem. However, for the one dimensional bin packing problem while there is a reduction in computational cost, the quality of the solutions is worse. Future research will investigate the reason for this and evaluate transferring different types of knowledge at various points in the life cycle of ant colony optimization generation construction hyper-heuristics.}, keywords = {Ant colony optimization, Processor scheduling, Transfer learning, Genetic programming, Evolutionary computation, Motion pictures, Computational efficiency, transfer learning, ant colony optimization, generation constructive hyper-heuristic, discrete optimization}, doi = {doi:10.1109/CEC55065.2022.9870415}, notes = {Also known as \cite{9870415}}, ) @INPROCEEDINGS(Hao:2022:CEC, %xplor 24 Sep 2022 author = {Hao Hao and Shuai Wang and Bingdong Li and Aimin Zhou}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Surrogate Model Assisted Estimation of Distribution Algorithm with Mutil-acquisition Functions for Expensive Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The estimation of distribution algorithm (EDA) is an efficient heuristic method for handling black-box optimization problems since the ability for global population distribution modeling and gradient-free searching. However, the trial and error search mechanism relies on a large number of function evaluations, which is a considerable challenge under expensive black-box problems. Therefore, this article presents a surrogate assisted EDA with multi-acquisition functions. Firstly, a variable-width histogram is used as the global distribution model that focuses on promising areas. Next, the evaluated-free local search method improves the quality of new generation solutions. Fi-nally, model management with multiple acquisitions maintains global and local exploration preferences. Several commonly used benchmark functions with 20 and 50 dimensions are adopted to evaluate the proposed algorithm compared with several state-of-the-art surrogate assisted evaluation algorithms (SAEAs) and Bayesian optimization method. In addition, a rover trajectories optimizing problem is used to verify the ability to solve complex problems. The experimental results demonstrate the superiority of the proposed algorithm over these comparison algorithms.}, keywords = {Histograms, Heuristic algorithms, Sociology, Estimation, Optimization methods, Focusing, Evolutionary computation, Surrogate assisted Evolutionary Algorithm, Estimation of distribution algorithm, Acquisition functions}, doi = {doi:10.1109/CEC55065.2022.9870436}, notes = {Also known as \cite{9870436}}, ) @INPROCEEDINGS(Zhang:2022:CEC, %xplor 24 Sep 2022 author = {Tianci Zhang and Lilla Beke and Songwei Liu and Michal Weiszer and Jun Chen}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An extended memetic algorithm for multiobjective routing and scheduling of airport ground movements with intermediate holding}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Routing and scheduling of airport ground movements poses a critical issue for efficient surface operations. For real-world applications, multiple objectives should be considered, leading to a multigraph representation of the search space. Meanwhile, intermediate holding is often needed to a) release availability of scarce resources such as runways and gates for more cost-effective routing and scheduling, b) provide additional solutions in the speed profile database that can be used during routing and scheduling, and c) keep airport ground movements functional during disruptive events that may paralyse part of the taxiway network. This paper presents an extended multiobjective memetic algorithm upon the multigraph model to search for desirable solutions with intermediate holding. The performance of the proposed algorithm is examined with problem instances of different airport layouts. The results demonstrate prominent savings in both time and fuel costs compared with solutions without intermediate holding.}, keywords = {Memetics, Costs, Databases, Logic gates, Airports, Routing, Scheduling, Airport ground movements, intermediate holding, memetic algorithm, multigraph, multiobjective optimisation}, doi = {doi:10.1109/CEC55065.2022.9870239}, notes = {Also known as \cite{9870239}}, ) @INPROCEEDINGS(Meikari:2022:CEC, %xplor 24 Sep 2022 author = {Junsei Meikari and Ryosuke Saga}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Node Layout and Edge Bundling}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper proposes a method that includes the process of adjusting the position of nodes in edge bundling by integrating an edge bundling and a node layout into a single genetic algorithm. Edge bundling is one of the graph visualization methods that can improve complex graphs and produce visually superior graphs. However, most edge bundling don't include the process of adjusting the position of nodes. Our approaches includes the process of adjusting the position of nodes in edge bundling by integrating an edge bundling and a node layout into genetic algorithm. Then, we apply our method to graphs and evaluate the generated graphs to investigate the effectiveness of our method.}, keywords = {Visualization, Layout, Evolutionary computation, Position measurement, Bending, Time measurement, Genetic algorithms, evolutionary computing, edge bundling, Graph drawings, Visualization}, doi = {doi:10.1109/CEC55065.2022.9870416}, notes = {Also known as \cite{9870416}}, ) @INPROCEEDINGS(Smedberg:2022:CEC, %xplor 24 Sep 2022 author = {Henrik Smedberg and Sunith Bandaru}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Modular Knowledge-Driven Mutation Operator for Reference-Point Based Evolutionary Algorithms}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Although an entire frontier of Pareto-optimal solutions exists for multi-objective optimization problems, in practice, decision makers are often only interested in a small subset of these solutions, called the region of interest. Specialized optimizers, such as reference-point based evolutionary algorithms, exist that can focus the search to only find solutions inside this region of interest. These algorithms typically only modify the selection mechanism of regular multi-objective optimizers to preferentially select solutions that conform to the reference point. However, a more effective search may be performed by additionally modifying the variation mechanism of the optimizers, namely the crossover and the mutation operators, to preferentially generate solutions conforming to the reference point. In this paper, we propose a modular mutation operator that uses a recent knowledge discovery technique to first find decision rules unique to the preferred solutions in each generation. These rules are then used to build an empirical distribution in the decision space that can be sampled to generate new mutated solutions which are more likely to be closer to the preferred solutions. The operator is modular in the sense that it can be used with any existing reference-point based evolutionary algorithm by simply replacing the mutation operator. We incorporate the proposed knowledge-driven mutation operator into three such algorithms, and through benchmark test problems up to 10 objectives, demonstrate that their performance improves significantly in the majority of cases according to two different performance indicators.}, keywords = {Evolutionary computation, Benchmark testing, Knowledge discovery, Optimization, Convergence, multi-objective optimization, reference point, knowledge discovery, mutation operator}, doi = {doi:10.1109/CEC55065.2022.9870268}, notes = {Also known as \cite{9870268}}, ) @INPROCEEDINGS(Chang:2022:CEC, %xplor 24 Sep 2022 author = {Yi-Feng Chang and Chuan-Kang Ting}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multiple Crossover and Mutation Operators Enabled Genetic Algorithm for Non-slicing VLSI Floorplanning}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Floorplanning is a crucial process in the early stage of VLSI physical design. It determines the performance, reliability, and size of chips. B*-tree is a simple yet efficient representation that encodes the layout of modules in a compact and non-slicing structure. Several B*-tree variants and corresponding operators have been proposed to deal with non-slicing floorplanning. However, these operators are considered and applied individually. A collective manipulation of them remains missing. This study proposes a genetic algorithm (GA) that enables multiple crossover and mutation operators for solving the non-slicing floorplanning problem. In particular, the GA selects one crossover operator and one mutation operator from the pool of operators whenever reproducing an offspring. The probability for an operator to be selected is based on its empirical performance. This study conducts experiments on two well-known benchmarks to examine the effectiveness of the proposed method. The experimental results show that the GA can achieve superior solution quality and efficiency on the non-slicing VLSI floorplanning.}, keywords = {Costs, Heuristic algorithms, Layout, Evolutionary computation, Very large scale integration, Benchmark testing, Reliability, VLSI floorplanning, B*-tree, genetic algorithm, multiple operators, selection strategy}, doi = {doi:10.1109/CEC55065.2022.9870396}, notes = {Also known as \cite{9870396}}, ) @INPROCEEDINGS(Lu:2022:CEC, %xplor 24 Sep 2022 author = {Meng-Huan Lu and Yu-Wei Wen and Chuan-Kang Ting}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Searching Crease Patterns by Genetic Algorithm for Origami Design}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Origami is a folding technique that can be used to create paper models. This technique has diverse applications in art, engineering, and medical devices, such as tessellations, deployable structures, and venous stents. Recently, a number of approaches have been developed to generate crease patterns for desired paper models. Most of the existing methods are specially designed to handle one or a few certain types of topologies and thus have limited applicability. To address this issue, this study proposes a genetic algorithm (GA) to generate crease patterns for orthogonal-structure origami models. Two crease pattern representations, i.e., string and matrix, are designed for the GA to deal with crease pattern search. The experimental results on three test instances indicate that the GA is capable of finding the target crease patterns. In particular, the string representation leads to faster convergence than the matrix representation. These outcomes show the potential of GA for origami design.}, keywords = {Medical devices, Shape, Computational modeling, Evolutionary computation, Search problems, Topology, Flexible structures, Crease pattern, orthogonal structure, genetic algorithm, representation}, doi = {doi:10.1109/CEC55065.2022.9870291}, notes = {Also known as \cite{9870291}}, ) @INPROCEEDINGS(Kitamura:2022:CEC, %xplor 24 Sep 2022 author = {Tomofumi Kitamura and Alex Fukunaga}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Differential Evolution with an Unbounded Population}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The notion of a population with individuals which are replaced by newly generated individuals is a pervasive idea in differential evolution (DE). Techniques such as archives have been previously proposed to supplement the central idea of a population, motivated by improved performance compared to a simple population structure. We consider Unbounded DE (UDE), which uses a population where individuals are never replaced, and the population monotonically grows as new individuals are generated. Behaviors similar to standard populations, as well as previous population modifications such as archives can be implemented within the UDE framework by implementing specific selection policies. We show experimentally that UDE can be configured to be competitive with standard DE as well as adaptive DE algorithms, showing that the traditional notion of replacement is not necessary for good performance.}, keywords = {Analytical models, Art, Sociology, Adaptive arrays, Evolutionary computation, Benchmark testing, Behavioral sciences, adaptive differential evolution, population, tournament selection}, doi = {doi:10.1109/CEC55065.2022.9870363}, notes = {Also known as \cite{9870363}}, ) @INPROCEEDINGS(Kitamura:2022:CEC, %xplor 24 Sep 2022 author = {Tomofumi Kitamura and Alex Fukunaga}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Duplicate Individuals in Differential Evolution}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Some modern computing architectures such as those which use GPUs offer significantly more computational capability in single-precision than double-precision floating point. However, when using single-precision math, evolutionary optimization algorithms such as differential evolution are much more prone to collision, where new individuals created by reproduction operators are duplicates of previously generated individuals. We show experimentally that on the CEC2014 single objective benchmarks and the BBOB benchmarks, search using the SHADE adaptive DE can waste more than 2percent of the fitness evaluations due to collisions. We (1) discuss the causes of this collision, (2) detect collisions using hash to avoid unnecessary evaluations, and (3) propose a restart method based on collisions.}, keywords = {Memory management, Evolutionary computation, Benchmark testing, Search problems, Linear programming, Frequency diversity, Heterogeneous networks, differential evolution, float precision, restart mechanism}, doi = {doi:10.1109/CEC55065.2022.9870366}, notes = {Also known as \cite{9870366}}, ) @INPROCEEDINGS(Kromer:2022:CEC, %xplor 24 Sep 2022 author = {Pavel Kromer and Vojtech Uher}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimization of real-world supply routes by nature-inspired metaheuristics}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The traveling salesman problem (TSP) is an iconic permutation problem with a number of applications in planning, scheduling, and logistics. It has also attracted much attention as a benchmarking problem frequently used to assess the properties of a variety of nature-inspired optimization methods. However, the standard libraries of TSP instances, such as the TSPLIB, are often decades old and might not reflect the requirements of modern real-world applications very well. In this work, we introduce several novel TSP instances representing real-world locations of pharmacies in several major cities of the Czech Republic. We look for the optimum routes between the pharmacies by selected nature-inspired algorithms and compare the results obtained on the real-world instances with their results on standard TSPLIB instances.}, keywords = {Ranking (statistics), Statistical analysis, Metaheuristics, Urban areas, Benchmark testing, Traveling salesman problems, Libraries, combinatorial optimization, real-world applications, traveling salesman problem, benchmarking}, doi = {doi:10.1109/CEC55065.2022.9870405}, notes = {Also known as \cite{9870405}}, ) @INPROCEEDINGS(Mamun:2022:CEC, %xplor 24 Sep 2022 author = {Mohammad Mohiuddin Mamun}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Worst Case Performance Based Optimization Involving Multifidelity Functions}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The paradigm of multifidelity optimization have received widespread attention in recent times to reduce computational budget of expensive simulations such as computational fluid dynamics (CFD), finite element analysis (FEA) etc. since it can abort a pre-converged simulations at different fidelity levels yet report optimum solutions. However, effects of perturbations in design variables i.e. robust optimization in terms of mutltifidlity optimization have yet to be investigated in detail. Typically robust optimization requires high number of function evaluations because of neighbourhood solution estimations. This study proposes a novel technique of multifidelity evolutionary optimization that incorporates robustness measures of optimum solutions involving iterative solvers of pre-converged simulations to reduce computational overhead. A probabilistic dominance based sorting mechanism is used to rank promising solutions of a generation where primary merit for sorting is worst case performance (WCP) of neighbourhood solutions of a solution of interest. Since estimation of WCP of neighbourhood solutions is computationally prohibitive, Kriging surrogate model is used for prediction. An artificial 1D multipeak multifidelity robust problem is formulated and the performance of proposed algorithm is illustrated with it. Afterwards, two additional mathematical functions and a practical engineering simulation problem of flapping wing kinematics have been solved. The results indicate the proposed algorithm is efficient to identify robust solutions in pre-converged simulations of iterative solvers.}, keywords = {Analytical models, Computational modeling, Computational fluid dynamics, Estimation, Prediction algorithms, Probabilistic logic, Mathematical models}, doi = {doi:10.1109/CEC55065.2022.9870319}, notes = {Also known as \cite{9870319}}, ) @INPROCEEDINGS(Goschen:2022:CEC, %xplor 24 Sep 2022 author = {Jarrod Goschen and Anna S. Bosman and Stefan Gruner}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic Micro-Programs for Automated Software Testing with Large Path Coverage}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Ongoing progress in computational intelligence (CI) has led to an increased desire to apply CI techniques for the pur-pose of improving software engineering processes, particularly software testing. Existing state-of-the-art automated software testing techniques focus on utilising search algorithms to discover input values that achieve high execution path coverage. These algorithms are trained on the same code that they intend to test, requiring instrumentation and lengthy search times to test each software component. This paper outlines a novel genetic programming framework, where the evolved solutions are not input values, but micro-programs that can repeatedly generate input values to efficiently explore a software component's input parameter domain. We also argue that our approach can be generalised such as to be applied to many different software systems, and is thus not specific to merely the particular software component on which it was trained.}, keywords = {Software testing, Codes, Instruments, Software algorithms, Genetic programming, Evolutionary computation, Software systems, Software testing, input domain partitioning, genetic programming, automated data generation}, doi = {doi:10.1109/CEC55065.2022.9870310}, notes = {Also known as \cite{9870310}}, ) @INPROCEEDINGS(Lim:2022:CEC, %xplor 24 Sep 2022 author = {Ray Lim and Abhishek Gupta and Yew-Soon Ong}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An Initial Investigation of Data-Lean Transfer Evolutionary Optimization with Probabilistic Priors}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Transfer evolutionary optimization (TrEO) has emerged as a computational paradigm to leverage related problem-solving information from various source tasks to boost convergence rates in a target task. State-of-the-art Tr EO algorithms have utilized a source-target similarity capture method with probabilistic priors that grants the ability to reduce negative transfers. A recent work makes use of an additional solution representation learning module to induce high ordinal correlation between source and target objective functions through source-to-target search space mappings, with the aim of promoting positive transfers between them. However, current implementations of this approach are found to be data-intensive - calling for all generated source data to be cached - leading to high storage costs in practice. As an alternative, this paper investigates the feasibility of a data-lean variant of the aforesaid approach, labeled as (1, G)-TrEO, in which only the first and final (Gth) generations of source data are used for solution representation learning and transfer. We conduct experimental analyses of (1, G)-TrEO using multi-objective benchmark functions as well as a practical example in vehicle crashworthiness design. Our results show that a simple data-lean transfer optimizer is able to achieve competitive performance. While this paper presents a first investigation of (1, G)-TrEO, we hope that the findings would inspire future forms of data-lean TrEO algorithms.}, keywords = {Representation learning, Costs, Benchmark testing, Probabilistic logic, Search problems, Task analysis, Optimization, transfer evolutionary optimization, solution representation learning, data-lean knowledge transfer, probabilistic model-based transfer, multi-objective problems}, doi = {doi:10.1109/CEC55065.2022.9870407}, notes = {Also known as \cite{9870407}}, ) @INPROCEEDINGS(Tong:2022:CEC, %xplor 24 Sep 2022 author = {Hao Tong and Leandro L. Minku and Stefan Menzel and Bernhard Sendhoff and Xin Yao}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Benchmarking Dynamic Capacitated Arc Routing Algorithms Using Real-World Traffic Simulation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The dynamic capacitated arc routing problem (DCARP) aims at re-scheduling the service plans of agents, such as vehicles in a city scenario, when dynamic events deteriorate the quality of the current schedule. Various algorithms have been proposed to solve DCARP instances in different dynamic scenarios. However, most existing work evaluated their algorithms' performance based on artificially constructed dynamic environments instead of using more realistic traffic simulations which are built on actual traffic data. In this paper, we constructed a novel DCARP benchmarking framework based on the Simulation of Urban MObility (SUMO) transportation simulation software, which allows to include real-world traffic environments for generating a set of DCARP instances from dynamic events, such as road congestion or task changes. The flexibility of the framework allows to develop DCARP optimization algorithms and evaluate their effectiveness more comprehensively. We use the benchmarking framework to generate 12 different dynamic instances using real-world traffic data of Dublin City. We then demonstrate the value of our framework by using these instances to compare our previously proposed hybrid local search algorithm (HyLS) with a state-of-the-art meta-heuristic optimization algorithm. The generated benchmark scenarios indicate that HyLS is a very effective optimizer on DCARP scenarios with real traffic data for reducing the total service cost. They also demonstrate the importance of our DCARP benchmarking framework for the development and benchmarking of optimization algorithms in more realistic scenarios.}, keywords = {Heuristic algorithms, Software algorithms, Urban areas, Transportation, Benchmark testing, Traffic control, Routing, Dynamic capacitated arc routing problem, Online optimization, Real-world application, SUMO, Meta-heuristic algorithms}, doi = {doi:10.1109/CEC55065.2022.9870399}, notes = {Also known as \cite{9870399}}, ) @INPROCEEDINGS(Chavali:2022:CEC, %xplor 24 Sep 2022 author = {Lalitha Chavali and Tanay Gupta and Paresh Saxena}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={{SAC-AP:} Soft Actor Critic based Deep Reinforcement Learning for Alert Prioritization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Intrusion detection systems (IDS) generate a large number of false alerts which makes it difficult to inspect true positives. Hence, alert prioritization plays a crucial role in deciding which alerts to investigate from an enormous number of alerts that are generated by IDS. Recently, deep reinforcement learning (DRL) based deep deterministic policy gradient (DDPG) off-policy method has shown to achieve better results for alert prioritization as compared to other state-of-the-art methods. However, DDPG is prone to the problem of overfitting. Additionally, it also has a poor exploration capability and hence it is not suitable for problems with a stochastic environment. To address these limitations, we present a soft actor-critic based DRL algorithm for alert prioritization (SAC-AP), an off-policy method, based on the maximum entropy reinforcement learning framework that aims to maximize the expected reward while also maximizing the entropy. Further, the interaction between an adversary and a defender is modeled as a zero-sum game and a double oracle framework is utilized to obtain the approximate mixed strategy Nash equilibrium (MSNE). SAC-AP finds robust alert investigation policies and computes pure strategy best response against opponent's mixed strategy. We present the overall design of SAC-AP and evaluate its performance as compared to other state-of-the art alert prioritization methods. We consider defender's loss, i.e., the defender's inability to investigate the alerts that are triggered due to attacks, as the performance metric. Our results show that SAC-AP achieves up to 3percent decrease in defender's loss as compared to the DDPG based alert prioritization method and hence provides better protection against intrusions. Moreover, the benefits are even higher when SAC-AP is compared to other traditional alert prioritization methods including Uniform, GAIN, RIO and Suricata.}, keywords = {Measurement, Art, Intrusion detection, Stochastic processes, Reinforcement learning, Games, Evolutionary computation, Alert Prioritization, Deep Reinforcement Learning (DRL), Soft Actor Critic (SAC), Mixed Strategy Nash Equilibrium (MSNE)}, doi = {doi:10.1109/CEC55065.2022.9870423}, notes = {Also known as \cite{9870423}}, ) @INPROCEEDINGS(Ruddick:2022:CEC, %xplor 24 Sep 2022 author = {Julian Ruddick and Evgenii Genov and Luis Ramirez Camargo and Thierry Coosemans and Maarten Messagie}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Scheduling of University Activities Based on Consumption Forecasts to Minimise Electricity Costs}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper presents a solution to a predict then optimise problem which goal is to reduce the electricity cost of a university campus. The proposed methodology combines a multi-dimensional time series forecast and a novel approach to large-scale optimization. Gradient-boosting method is applied to forecast both generation and consumption time-series of the Monash university campus for the month of November 2020. For the consumption forecasts we employ log transformation to model trend and stabilize variance. Additional seasonality and trend features are added to the model inputs when applicable. The forecasts obtained are used as the base load for the schedule optimisation of university activities and battery usage. The goal of the optimisation is to minimize the electricity cost consisting of the price of electricity and the peak electricity tariff both altered by the load from class activities and battery use as well as the penalty of not scheduling some optional activities. The schedule of the class activities is obtained through evolutionary optimisation using the covariance matrix adaptation evolution strategy and the genetic algorithm. This schedule is then improved through local search by testing possible times for each activity one-by-one. The battery schedule is formulated as a mixed-integer programming problem and solved by the Gurobi solver. This method obtains the second lowest cost when evaluated against 6 other methods presented at an IEEE competition that all used mixed-integer programming and the Gurobi solver to schedule both the activities and the battery use. The code and data used for the paper are publicly available1.}, keywords = {Schedules, Costs, Processor scheduling, Time series analysis, Optimal scheduling, Programming, Predictive models, evolutionary scheduling, load forecasting, covariance matrix adaptation evolution strategy (CMA-ES), genetic algorithm, mixed-integer programming, evolutionary algorithms, demand response}, doi = {doi:10.1109/CEC55065.2022.9870213}, notes = {Also known as \cite{9870213}}, ) @INPROCEEDINGS(Marcelino:2022:CEC, %xplor 24 Sep 2022 author = {Carolina G. Marcelino and Elizabeth F. Wanner and Flavio V. C. Martins and Jorge Perez-Aracil and Silvia Jimenez-Fernandez and Sancho Salcedo-Sanz}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Solving the Optimal Active-Reactive Power Dispatch Problem in Smart Grids with the C-DEEPSO Algorithm}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Optimal active-reactive power dispatch problems (OARPD) are considered large scale optimization problems with a high nonlinear complexity. Usually, in OARPD the objective is to minimize the cost of the system operation. In 2018, the IEEE PES committee proposed a competition, the "Operational planning of sustainable power systems", in which a test bed relating the OARPD and a renewable energy generation challenge within a smart grid was proposed. In this work we consider three test scenarios proposed in that competition. Specifically, we present a hybrid meta-heuristic optimization approach applied to the OARPD, the Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO), to tackle these test scenarios. Comparative results with other algorithms such as CMA-ES, EPSO, and CEEPSO indicate that C-DEEPSO shows a competitive performance when solving the OARPD problems.}, keywords = {Renewable energy sources, Systems operation, Metaheuristics, Wind power generation, Generators, Smart grids, Space exploration, Evolutionary algorithms, C-DEEPSO, OARPD, Smart Grids}, doi = {doi:10.1109/CEC55065.2022.9870385}, notes = {Also known as \cite{9870385}}, ) @INPROCEEDINGS(Dantas:2022:CEC, %xplor 24 Sep 2022 author = {Lucas H. A. Dantas and Romerito C. Andrade and Leonardo C. T. Bezerra}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={High school timetabling at a federal educational institute in Brazil}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={High school timetabling (HST) is a relevant problem traditionally addressed by (meta)heuristic approaches. This work addresses the HST in the context of the technical courses offered at the Instituto Federal de Educacao, Ciencia e Tecnologia do Rio Grande do Norte (IFRN). Part of the largest Brazilian federal educational network, IFRN comprises 22 campi located in 18 different cities, which makes an HST approach for IFRN challenging, critical, and potentially seminal for other institutes of the network. Our contributions are two-fold. First, we model the HST problem at IFRN both as to mathematical formulation and real-world instances, which we create from data gathered at different campi. Second, we propose a greedy randomized adaptive search procedure (GRASP) algorithm specific for this scenario. To validate our contributions, we benchmark on the instances we create (i) state-of-the-art, (ii) commercial, and (iii) the proposed GRASP algorithms. Our approach produces feasible solutions for more instances than the remaining algorithms, with also competitive solution quality.}, keywords = {Urban areas, Production, Pressing, Evolutionary computation, Mathematical models, Data models, Windows, high school timetabling, metaheuristic, GRASP}, doi = {doi:10.1109/CEC55065.2022.9870388}, notes = {Also known as \cite{9870388}}, ) @INPROCEEDINGS(Mariot:2022:CEC, %xplor 24 Sep 2022 author = {Luca Mariot and Stjepan Picek and Domagoj Jakobovic and Marko Djurasevic and Alberto Leporati}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Construction of Perfectly Balanced Boolean Functions}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Finding Boolean functions suitable for cryptographic primitives is a complex combinatorial optimization problem, since they must satisfy several properties to resist cryptanalytic attacks, and the space is very large, which grows super exponentially with the number of input variables. Recent research has focused on the study of Boolean functions that satisfy properties on restricted sets of inputs due to their importance in the development of the FLIP stream cipher. In this paper, we consider one such property, perfect balancedness, and investigate the use of Genetic Programming (GP) and Genetic Algorithms (GA) to construct Boolean functions that satisfy this property along with a good nonlinearity profile. We formulate the related optimization problem and define two encodings for the candidate solutions, namely the truth table and the weightwise balanced representations. Somewhat surprisingly, the results show that GA with the weightwise balanced representation outperforms GP with the classical truth table phenotype in finding highly nonlinear Weightwise Perfectly Balanced (WPB) functions. This is in stark contrast to previous findings on the evolution of balanced Boolean functions, where GP always performs best.}, keywords = {Ciphers, Boolean functions, Input variables, Genetic programming, Resists, Evolutionary computation, Programming, Boolean functions, balancedness, nonlinearity, genetic algorithms, genetic programming}, doi = {doi:10.1109/CEC55065.2022.9870427}, notes = {Also known as \cite{9870427}}, ) @INPROCEEDINGS(Pinheiro:2022:CEC, %xplor 24 Sep 2022 author = {Tiago F.D. Pinheiro and Santiago V. Ravelo and Luciana S. Buriol}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A fix-and-optimize matheuristic for the k-labelled spanning forest problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this paper, we study the k-labeled spanning forest problem (kLSF). The input for this problem is an undirected graph with labeled edges and a positive integer k. The goal is to find a spanning forest of the graph with at most $k$ different labels associated with the edges, minimizing the number of components. kLSF finds practical applications in different scenarios related to networks design and telecommunications. Solving it may help to reduce the negative impact of electromagnetic fields exposure on the population health or to increase profits of internet management companies, among others. The interest in kLSF is not only practical but also theoretical since the problem generalizes the best-known NP-hard minimum labeling spanning tree problem (MLST). To approach kLSF, we propose a fix-and-optimize matheuristic that was tested over several instances, achieving high-quality solutions in reasonable computational time. When compared to the best-known algorithms in the literature, our matheuristic outperformed the other proposals in most cases, finding better solutions in less computational time for the most challenging instances.}, keywords = {Sociology, Forestry, Evolutionary computation, Companies, Telecommunications, Proposals, Labeling, Fix-and-optimize, Matheuristic, Integer Linear Program, k-Labeled Spanning Forest}, doi = {doi:10.1109/CEC55065.2022.9870342}, notes = {Also known as \cite{9870342}}, ) @INPROCEEDINGS(Samarakoon:2022:CEC, %xplor 24 Sep 2022 author = {S. M. Bhagya P. Samarakoon and M. A. Viraj J. Muthugala and Mohan Rajesh Elara}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Global and Local Area Coverage Path Planner for a Reconfigurable Robot}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Area coverage is essential for robots used in cleaning, painting, and exploration applications. Reconfigurable robots have been introduced to solve the area coverage limitation of fixed-shape robots. The existing global coverage algorithms of reconfigurable robots are limited to consideration of a limited set of predefined shapes for the reconfiguration and do not consider the exact geometrical shape of obstacles. Therefore, degraded coverage performance could be observed from the existing methods. On the other hand, the coverage methods that consider reconfiguring beyond a limited set of predefined shapes are limited to local coverage. Furthermore, these methods only consider a single reconfiguration for the coverage. Therefore, this paper proposes a novel coverage method for a reconfigurable robot consisting of both global and local path planners. The global path planner uses boustrophedon motion combined with the A * algorithm. The optimum grid positioning that maximizes the global coverage is determined through a Genetic Algorithm (GA). The local coverage planner performs continuous reconfig-uration of the robot to adequately cover obstacle zones while navigating through narrow spaces without collisions. A GA is used to determine the reconfiguration parameters of the robot at each instance of the local coverage. Simulation results confirm that the proposed method is effective in performing both global and local coverage path planning for improving the area coverage performance.}, keywords = {Shape, Navigation, Simulation, Evolutionary computation, Path planning, Cleaning, Collision avoidance, Path planing, Area coverage, Genetic algorithm, Reconfigurable robotics}, doi = {doi:10.1109/CEC55065.2022.9870308}, notes = {Also known as \cite{9870308}}, ) @INPROCEEDINGS(Sitaru:2022:CEC, %xplor 24 Sep 2022 author = {Ioana Sitaru and Madalina Raschip}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Algorithm Selection for Combinatorial Packing Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper describes a way of building an algorithm selection system for the two-dimensional rectangle packing problem. A list of features was defined, and five heuristics and metaheuristics were selected as solvers. The first step involves the extraction of features from the input problem instances. Using those computed features, machine learning regressors will predict the performance of each solver on the instance and, therefore, find the most fitted algorithm for it. Both the predicted execution time and the expected quality of the solution were used as performance metrics for the selection step. The experimental results showed that the built system has a high accuracy in predicting the best algorithm based on the runtime execution and the solution quality. Moreover, the system achieves these results with much lower computational resources than running the actual solvers of the problem.}, keywords = {Training, Measurement, Strips, Machine learning algorithms, Runtime, Metaheuristics, Predictive models, automated algorithm selection, strip packing, runtime prediction, performance prediction}, doi = {doi:10.1109/CEC55065.2022.9870417}, notes = {Also known as \cite{9870417}}, ) @INPROCEEDINGS(Bujok:2022:CEC, %xplor 24 Sep 2022 author = {Petr Bujok and Patrik Kolenovsky}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Eigen Crossover in Cooperative Model of Evolutionary Algorithms Applied to CEC 2022 Single Objective Numerical Optimisation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this paper, a cooperative model of four well-performing evolutionary algorithms enhanced by Eigen crossover is proposed and applied to a set of problems CEC 2022. The four adaptive algorithms employed in this model are - Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Differen-tial Evolution with Covariance Matrix Learning and Bimodal Distribution Parameter Setting (CoBiDE), an adaptive variant of jSO, and Differential Evolution With an Individual-Dependent Mechanism (IDE). For the higher efficiency of the cooperative model, a linear population-size reduction mechanism is employed. The model was introduced for CEC 2019. Here, Eigen crossover is applied for each cooperating algorithm. The provided results show that the proposed model of four Evolutionary Algorithms with Eigen crossover (EA4eig) is able to solve ten out of 24 optimisation problems. Moreover, comparing EA4eig with four state-of-the-art variants of adaptive Differential Evolution illustrates the superiority of the newly designed optimiser.}, keywords = {Adaptation models, Computational modeling, Evolutionary computation, Benchmark testing, Numerical models, Optimization, Tuning, Differential Evolution, Evolution Strategy, co-operative model, competition, experiments, Eigen crossover}, doi = {doi:10.1109/CEC55065.2022.9870433}, notes = {Also known as \cite{9870433}}, ) @INPROCEEDINGS(Karn:2022:CEC, %xplor 24 Sep 2022 author = {Ravi Ranjan Prasad Karn and Rakesh Kumar Sanodiya and Twinkle Sharma and Shreshtha Sharan and Kritika Garg and Jimson Mathew and Leehter Yao}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Feature and Parameter Selection Approach for Visual Domain Adaptation using Particle Swarm Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={To train a classifier on a specific domain, often called the target domain, we need labeled data. However, there might be non availability of the labeled data in this domain. In this scenario, we look for a related domain called the source domain, where availability of labeled data is abundant in number. The lack of availability of labeled data in the target domain poses a serious problem and several domain adaptation (DA) approaches have been put forward to cope up with this problem. Existing DA methods seek a subspace common between both the domains (source and target domains) where the distribution difference is minimal and perform manual parameter sensitivity tests to find apposite value of each parameter for their respective objective function. However, for distorted original data, obtaining a common subspace is a challenging task and condensing manual parameter sensitivity testing is also costly and a time-intensive process. To overcome these challenges, some DA methods consider particle swarm optimization (PSO) technique. However, none of the existing DA methods simultaneously tackle these challenges. Therefore, in this paper, we put forward a method called Feature and Parameter Selection approach for visual Domain Adaptation (FPSDA) to address these challenges. In FPSDA, a suitable subset of features across both the domains and an apposite value of each parameter are simultaneously chosen using a PSO approach. Moreover, to guide the PSO, the objective functions of Joint Geometrical and Statistical Alignment (JGSA) [1] method along with preserving original similarity of data is considered as an objective function for our proposed approach. Full Scale experiments on benchmark datasets for cross-domain adaptation verify that FPSDA performs better than many state-of-the-art classic machine learning and domain adaptation approaches.}, keywords = {Visualization, Sensitivity, Art, Manuals, Machine learning, Benchmark testing, Linear programming, Visual Domain Adaptation, PSO, Feature Selection, Parameter Selection, Fitness Function}, doi = {doi:10.1109/CEC55065.2022.9870263}, notes = {Also known as \cite{9870263}}, ) @INPROCEEDINGS(Golabi:2022:CEC, %xplor 24 Sep 2022 author = {Mahmoud Golabi and Lhassane Idoumghar and Jamal Arkat}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A bi-objective single-server congested edge-based facility location problem under disruption}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This study proposes a new bi-objective mixed-integer non-linear mathematical model for an interruptible single-server congested facility location problem with uniformly distributed demands along the network edges. It is assumed that in the case of server disruption, all the waiting customers leave the facility without receiving the service, and there would be no entry until fixing the server. Limiting by the maximum waiting time threshold, this study aims to determine the number and locations of established facilities. The first objective function minimizes the facility establishment costs, while the second objective function is to minimize the aggregate traveling, waiting, and demand lost costs. Due to the NP-hardness nature of the problem, several state-of-the-art evolutionary multi-objective optimization (EMO) algorithms are applied to find the set of non-dominated solutions. The results indicate that the applied SPEA - II algorithm outperforms its competitors in the majority of generated test cases.}, keywords = {Costs, Limiting, Aggregates, Evolutionary computation, Linear programming, Mathematical models, Servers, Facility location problem, continuous demands, queuing systems, multi-objective optimization}, doi = {doi:10.1109/CEC55065.2022.9870356}, notes = {Also known as \cite{9870356}}, ) @INPROCEEDINGS(Gori:2022:CEC, %xplor 24 Sep 2022 author = {Valentina Gori and Giacomo Veneri and Valeria Ballarini}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Continual Learning for anomaly detection on turbomachinery prototypes - A real application}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={We apply a Recurrent Neural Network (RNN), Kullback-Leibler (KL) divergence and a continual learning approach to check the status of several hundreds of sensors during turbo-machinery prototype testing. Turbo-machinery prototypes can be instrumented with up to thousands of sensors. Therefore, checking the health of each sensor is a time consuming activity. Prototypes are also tested on several different and a-priori unknown operating conditions, so we cannot apply a purely supervised model to detect potential anomalies of sensors and, moreover, we have to take into account a covariate shift because measurements drift continuously day by day. We continuously train a RNN (daily) to build a virtual sensor from other sensors and we compare the predicted signal vs the real signal to raise (in case) an anomaly. Furthermore, KL is used to estimate the overlap between the input distributions available at training time and the ones seen at test time, and thus the confidence level of the prediction. Finally we implement an end-to-end system to automatically train and evaluate the models. The paper presents the system and reports the application to a test campaign of about five hundred sensors.}, keywords = {Recurrent neural networks, Green buildings, Soft sensors, Prototypes, Predictive models, Turbomachinery, Sensor systems}, doi = {doi:10.1109/CEC55065.2022.9870234}, notes = {Also known as \cite{9870234}}, ) @INPROCEEDINGS(Skvorc:2022:CEC, %xplor 24 Sep 2022 author = {Urban Skvorc and Tome Eftimov and Peter Korosec}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Comprehensive Analysis of the Invariance of Exploratory Landscape Analysis Features to Function Transformations}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Exploratory Landscape Analysis is a powerful technique that allows us to gain an understanding of a problem landscape solely by sampling the problem space. It has been successfully used in a number of applications, for example for the task of automatic algorithm selection. However, recent work has shown that Exploratory Landscape Analysis contains some specific weaknesses that its users should be aware of. As the technique is sample based, it has been shown to be sensitive to the choice of sampling strategy. Additionally, many landscape features are not invariant to transformations of the underlying samples which should have no effect on algorithm performance, specifically shifting and scaling. The analysis of the effect of shifting and scaling has so far only been demonstrated on a single problem set and dimensionality. In this paper, we perform a comprehensive analysis of the invariance of Exploratory Landscape Analysis features to these two transformations, by considering different sampling strate-gies, sampling sizes, problem dimensionalities, and benchmark problem sets to determine their individual and combined effect. We show that these factors have very limited influence on the features' invariance when they are considered either individually or combined.}, keywords = {Computer languages, Evolutionary computation, Benchmark testing, Task analysis, Optimization, Exploratory Landscape Analysis, Numerical Optimization, Benchmarking}, doi = {doi:10.1109/CEC55065.2022.9870313}, notes = {Also known as \cite{9870313}}, ) @INPROCEEDINGS(Yu:2022:CEC, %xplor 24 Sep 2022 author = {Xiang Yu and Li Zhang and Mei Shen}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Nantong Blue Calico Image Dataset and Its Recognition}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Nantong blue calico is a kind of important intangible cultural heritages in China. To better safeguard and inherit it in a digital way, it is necessary to construct a large-scale dataset for Nantong blue calico. As so far, however, we could not find a public dataset for blue calico. The goal of this paper is to give a public image dataset which named $N$ tBC consisting of Nantong blue calico patterns and provide a baseline result for the recognition of Nantong blue calico patterns. In this paper, we perform several baseline experiments on the NtBC dataset, including handcrafted and deep feature based classification methods. we compare some handcrafted methods and four kinds of popular convolutional neural networks (CNNs), including ResNet-50, AlexNet, GoogLeNet-V1 and VGGNet-16. Experimental results show that ResNet-50 yields an accuracy of 93.percent in the recognition performance, which shows that it is efficient to classify blue calico patterns through deep learning methods. As a consequence, this result provides the current best baseline result for Nantong blue calico image recognition. We believe our $N$ tBC will facilitate future research on Chinese traditional patterns development, fine grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/facebook/react.}, keywords = {Deep learning, Visualization, Image recognition, Evolutionary computation, Benchmark testing, Pattern recognition, Cultural differences, Intangible cultural heritage, Blue calico, Image recognition, Benchmark dataset, Deep learning}, doi = {doi:10.1109/CEC55065.2022.9870225}, notes = {Also known as \cite{9870225}}, ) @INPROCEEDINGS(Patelli:2022:CEC, %xplor 24 Sep 2022 author = {Alina Patelli and John Rego Hamilton and Victoria Lush and Aniko Ekart}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A {GENTLER} Approach to Urban Traffic Modelling and Prediction}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Intelligent Transportation aims to usher in a new and improved version of motorised traffic, one that is stream-lined, safe and at the heart of the net-zero agenda. Designing and building the urban infrastructure necessary to turn that vision into a reality relies on complex decision making, which often hinges on estimating the dynamics of future traffic through areas of the road network that are yet to be built. Traffic models capable of yielding such estimations, robustly and reliably, are valuable technological tools that urban planners can utilise to inform their decisions. To that end, we propose a novel algorithm that employs Genetic Programming and Transfer Learning to produce traffic models which accurately predict vehicle flow through a given junction based on readings collected from sur-rounding areas. We enhance the algorithm with a randomisation mechanism and run a comprehensive experimental study on a segment of the city of Darmstadt's road network, in order to investigate the effects of the exploration-exploitation interplay on the generated models' prediction accuracy.}, keywords = {Roads, Computational modeling, Urban areas, Transfer learning, Transportation, Predictive models, Prediction algorithms, Genetic Programming, Transfer Learning, Exploration-Exploitation Tuning, Intelligent Transportation, Traffic Prediction}, doi = {doi:10.1109/CEC55065.2022.9870273}, notes = {Also known as \cite{9870273}}, ) @INPROCEEDINGS(CROITORU:2022:CEC, %xplor 24 Sep 2022 author = {Eugen CROITORU and Alexandru-Denis CHIPARUS and Henri LUCHIAN}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Punctuated Equilibrium and Neutral Networks in Genetic Algorithms}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Taking focused inspiration from biological evolution, we present an empirical study which shows that a Simple Genetic Algorithm (SGA) exhibits punctuated equilibria and punctuated gradualism in its evolution. Using the concept of consensus sequences, and comparing genotype change to phenotype change, we show how an SGA explores candidate solutions along a neutral network - Hamming-proximal bitstrings of similar fit-ness. Alongside mapping the normal functioning of an SGA, we monitor the formation of error thresholds "from above" by starting with a high mutation probability and slowly lowering it, during hundreds of thousands of generations. The formation of a stable consensus sequence is marked by a measurable upheaval in the dynamics of the population, leading to an efficient exploration of the search space in a short time. After the global optimum is found, we can still measure the degree of exploration the SGA performs on that neutral network, and observe punctuated equilibria. We use 11 numerical benchmark functions, along with the Royal Road Function, and a similar bit block Trap Function; the phenomena observed are largely similar on all of them, pointing to a generic behaviour of Genetic Algorithms, rather than problem particularities. Using a consensus sequence (a per-locus-mode chromosome) obscures quasispecies dynamics. This is why we use a per-locus-mean chromosome to measure information change between successive generations, and plot the number and maximal size of Quasispecies and Neutral Networks.}, keywords = {Evolution (biology), Roads, Sociology, Size measurement, Particle measurements, Time measurement, Statistics, Genetic Algorithms, Neutral Networks, biological inspiration, numerical optimisation}, doi = {doi:10.1109/CEC55065.2022.9870348}, notes = {Also known as \cite{9870348}}, ) @INPROCEEDINGS(Hassan:2022:CEC, %xplor 24 Sep 2022 author = {Ahmed Hassan and Nelishia Pillay}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Hybridizing A Genetic Algorithm With Reinforcement Learning for Automated Design of Genetic Algorithms}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The automated design of optimization techniques holds great promise for advancing state-of-the-art optimization techniques and it has already taken over the manual design by human experts in some problems. Genetic algorithms are one of the key approaches for tackling the automated design problem. Unfortunately, these algorithms may take several hours to run as the fitness evaluation involves solving some benchmark instances to determine the quality of a candidate configuration. In this paper, we hybridize a meta-genetic algorithm with reinforcement learning to automatically design genetic algorithms for the two-dimensional bin packing problem. The task of the meta-genetic algorithm is to search the configuration space of genetic algorithms and the task of reinforcement learning is to decide whether to evaluate a candidate configuration or not. Therefore, avoiding wasting the computational budget on poor configurations. The proposed hybrid and the meta-genetic algorithm without reinforcement learning produce solvers for the two-dimensional bin packing problem that are competitive with the state-of-the-art algorithms. However, the proposed hybrid consumes about 2percent of the computational effort required by the meta-genetic algorithm without reinforcement learning.}, keywords = {Reinforcement learning, Manuals, Evolutionary computation, Benchmark testing, Task analysis, Optimization, Genetic algorithms, meta-genetic algorithms, reinforcement learning, automated design}, doi = {doi:10.1109/CEC55065.2022.9870302}, notes = {Also known as \cite{9870302}}, ) @INPROCEEDINGS(Maslyaev:2022:CEC, %xplor 24 Sep 2022 author = {Mikhail Maslyaev and Alexander Hvatov}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Solver-Based Fitness Function for the Data-Driven Evolutionary Discovery of Partial Differential Equations}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Partial differential equations provide accurate models for many physical processes, although their derivation can be challenging, requiring a fundamental understanding of the modeled system. This challenge can be circumvented with the data-driven algorithms that obtain the governing equation only using observational data. One of the tools commonly used in search of the differential equation is the evolutionary optimization algorithm. In this paper, we seek to improve the existing evolutionary approach to data-driven partial differential equation discovery by introducing a more reliable method of evaluating the quality of proposed structures, based on the inclusion of the automated algorithm of partial differential equations solving. In terms of evolutionary algorithms, we want to check whether the more computationally challenging fitness function represented by the equation solver gives the sufficient resulting solution quality increase with respect to the more simple one. The approach includes a computationally expensive equation solver compared with the baseline method, which utilized equation discrepancy to define the fitness function for a candidate structure in terms of algorithm convergence and required computational resources on the synthetic data obtained from the solution of the Korteweg-de Vries equation.}, keywords = {Partial differential equations, Computational modeling, Evolutionary computation, Linear programming, Mathematical models, Reliability, Noise measurement, equation discovery, partial differential equation, fitness function selection, data-driven modelling}, doi = {doi:10.1109/CEC55065.2022.9870370}, notes = {Also known as \cite{9870370}}, ) @INPROCEEDINGS(Tuan-Anh:2022:CEC, %xplor 24 Sep 2022 author = {Do {Tuan Anh} and Nguyen {Hoang Long} and Tran {Van Diep} and Huynh {Thi Thanh Binh}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Genetic Ant Colony Optimization Algorithm for Inter-domain Path Computation problem under the Domain Uniqueness constraint}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={For the past few years, Hierarchical Path Computation Element (h-PCE) is an architecture that has been promoted to handle packet routing in multi-domain networks. However, this architecture has a potential drawback of poor scalability with respect to the number of domains. In tackling this complicated problem, we focuses on Inter-Domain Path Computation problem under the Domain Uniqueness constraint (IDPC-DU), which is employed to improve h-PCE. The objective of IDPC-DU is to find the shortest path between two given nodes that traverses every domain at most once. Since the IDPC-DU belongs to NP-Hard class, this paper introduces a two-level approach, combining the advantages of two metaheuristic algorithms, Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Specifically, the upper-level GA plays the role of navigating the path for ants at the lower level ACO. Furthermore, an anti-stuck strategy that helps ants avoid being stuck is also equipped. To analyze the effectiveness of the proposed algorithm, experiments and comparisons with other algorithms are conducted. The results demonstrated that the proposed algorithm outperforms all other comnared ones in most cases.}, keywords = {Ant colony optimization, Navigation, Scalability, Metaheuristics, Computer architecture, Evolutionary computation, Routing, Multi-Domain, Path Computation Element, Evolutionary Algorithm, Ant Colony Optimization}, doi = {doi:10.1109/CEC55065.2022.9870339}, notes = {Also known as \cite{9870339}}, ) @INPROCEEDINGS(Hassan:2022:CEC, %xplor 24 Sep 2022 author = {Ahmed Hassan and Nelishia Pillay}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Automated Design of Hybrid Metaheuristics: A Fitness Landscape Analysis}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The automated design of search techniques is a recent trend in artificial intelligence research. Unfortunately, the majority of the automated design approaches are developed using trial and error which fails to justify or at least explain why some design decisions succeed while others fail. This approach is a host of evils as it has resulted in poorly understood systems for poorly understood problems. This study is an attempt to improve our understanding of the automated design of hybrid metaheuristics by utilizing fitness landscape analysis to reveal the topological characteristics that can be exploited to design better automated approaches. We consider the sequential hybridization, including algorithm configuration and parameter tuning, of single-point and multi-point metaheuristics and three optimization problems which are the earth-observing satellite scheduling problem, the aircraft landing problem and the two-dimensional bin packing problem. Interestingly, the design space exhibits similar trends regardless of the underlining optimization problem. The design space is found to be rugged, multimodal, moderately searchable, has multiple funnels, and almost no plateau. Based on these findings, deeper insights are provided to guide the development of future automated approaches instead of blindly trying different options.}, keywords = {Satellites, Metaheuristics, Evolutionary computation, Market research, Scheduling, Artificial intelligence, Aircraft, fitness landscape analysis, hybrid metaheuristics, automated design}, doi = {doi:10.1109/CEC55065.2022.9870231}, notes = {Also known as \cite{9870231}}, ) @INPROCEEDINGS(Baumann:2022:CEC, %xplor 24 Sep 2022 author = {Cyrill Baumann and Alcherio Martinoli}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The automatic design of well-performing robotic controllers is still an unsolved problem due to the inherently large parameter space and noisy, often hard-to-define performance metrics, especially when sequential tasks need to be accomplished. Distal control architectures, which combine pre-coded basic behaviors into a (probabilistic) finite state machine offer a promising solution to this problem. In this paper, we enhance a Mixed-Discrete Particle Swarm Optimization (MDPSO) algorithm with an Optimal Computing Budget Allocation (OCBA) scheme to automatically synthesize distal control architectures. We benchmark MDPSO-OCBA's performance against the original MDPSO as well as the Iterated F-Race (IRACE) and the Mesh Adaptive Direct Search (MADS) algorithms on both a benchmark function with different noise levels and design problems of distal control architectures. More specifically, we evaluate the algorithms using high-fidelity simulations in three increasingly challenging scenarios involving parallel and sequential tasks. Additionally, the best performing controller generated in simulation by each optimization algorithm is compared with a manually designed solution and validated with physical experiments. The analysis on the benchmark function with different noise levels demonstrates MDPSO-OCBA's high robustness to noise. The comparison on the robotic control design problems shows that, without any meta-parameter tuning, MDPSO-OCBA is able to generate the best performing control architectures overall, closely followed by IRACE. They significantly outperform MADS for the more complex and noisier scenarios, resulting in competitive controllers in comparison to the manually designed one.}, keywords = {Adaptation models, Computer architecture, Benchmark testing, Noise measurement, Resource management, Particle swarm optimization, Task analysis}, doi = {doi:10.1109/CEC55065.2022.9870229}, notes = {Also known as \cite{9870229}}, ) @INPROCEEDINGS(Biedrzycki:2022:CEC, %xplor 24 Sep 2022 author = {Rafal Biedrzycki and Jaroslaw Arabas and Eryk Warchulski}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Version of {NL-SHADE-RSP} Algorithm with Midpoint for {CEC} 2022 Single Objective Bound Constrained Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper presents an enhanced version of NL-SHADE-RSP, which won CEC'2021 competition on single objective bound-constrained numerical optimization for shifted and rotated shifted functions. The proposed version uses the midpoint of the population to estimate the optimum. The midpoint fitness is also used to introduce a restart trigger. For large populations, the midpoint is calculated after splitting the population into two parts by the k-means algorithm. Other introduced modifications include changing the bound constrain handling method and reducing population size. The performance of the proposed approach is evaluated on the CEC 2022 benchmark for single objective bound-constrained numerical optimization. The results confirm that each proposed modification gradually improves the algorithm's ranking on the benchmark.}, keywords = {Constraint handling, Sociology, Evolutionary computation, Benchmark testing, Statistics, Optimization, differential evolution, CEC 2022, midpoint, resampling}, doi = {doi:10.1109/CEC55065.2022.9870220}, notes = {Also known as \cite{9870220}}, ) @INPROCEEDINGS(Mousavirad:2022:CEC, %xplor 24 Sep 2022 author = {Seyed Jalaleddin Mousavirad and Amir H. Gandomi and Hassan Homayoun}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Clustering-based Differential Evolution Boosted by a Regularisation-based Objective Function and a Local Refinement for Neural Network Training}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The performance of feed-forward neural networks (FFNN) is directly dependant on the training algorithm. Conventional training algorithms such as gradient-based approaches are so popular for FFNN training, but they are susceptible to get stuck in local optimum. To overcome this, population-based metaheuristic algorithms such as differential evolution (DE) are a reliable alternative. In this paper, we propose a novel training algorithm, Reg-IDE, based on an improved DE algorithm. Weight regularisation in conventional algorithms is an approach to reduce the likelihood of over-fitting and enhance generalisation. However, to the best of our knowledge, the current DE-based trainers do not employ regularisation. This paper, first, proposes a regularisation-based objective function to improve the generalisation of the algorithm by adding a new term to the objective function. Then, a region-based strategy determines some regions in search space using a clustering algorithm and updates the population based on the information available in each region. In addition, quasi opposition-based learning enhances the exploration of the algorithm. The best candidate solution found by improved DE is then used as the initial network weights for the Levenberg-Marquardt (LM) algorithm, as a local refinement. Experimental results on different benchmarks and in comparison with 26 conventional and population-based approaches apparently demonstrate the excellent performance of Reg-IDE.}, keywords = {Training, Neural networks, Sociology, Metaheuristics, Clustering algorithms, Evolutionary computation, Linear programming, Neural networks, differential evolution, regularisation, Levenberg-Marquardt algorithm, clustering, opposition-based learning}, doi = {doi:10.1109/CEC55065.2022.9870211}, notes = {Also known as \cite{9870211}}, ) @INPROCEEDINGS(Rihane:2022:CEC, %xplor 24 Sep 2022 author = {Karima Rihane and Adel Dabah and Abdelhakim AitZai}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Learning-based Selection process for Branch and Bound Algorithms}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Branch and Bound (B&B) algorithms represent a well-known tool for optimally solving combinatorial optimization problems in general and scheduling problems in particular. They have been widely used as a reference for classical scheduling problems such-as job shop scheduling. However, they rely on a deep knowledge of the problem to achieve fast convergence and low execution time. To deal efficiently with real-world problems where we do not have prior knowledge, we investigate the use of learning-based methods to accelerate the B&B tree exploration in this paper. We use the Blocking Job Shop Scheduling Problem (BJSSP) as a study case. Our approach aims to learn an efficient branching and selection process by training a model using small and medium BJSSP benchmarks. For each benchmark, two phases are used. First, we train the model using a small set of solutions provided by a metaheuristic to detect similar features among good solutions. Therefore, generating branching and selection roles. After that, we apply these roles to solve these instances optimally. The obtained results show the effectiveness of our proposed learning-based selection by achieving performance results near the best B&B implementation for BJSSP known to us.}, keywords = {Training, Learning systems, Job shop scheduling, Machine learning algorithms, Metaheuristics, Machine learning, Evolutionary computation, Job Shop Scheduling Problem, Blocking Constraint, Branch and Bound, Learning selection}, doi = {doi:10.1109/CEC55065.2022.9870384}, notes = {Also known as \cite{9870384}}, ) @INPROCEEDINGS(Carrero:2022:CEC, %xplor 24 Sep 2022 author = {Jonathan Carrero and Ismael Rodriguez and Fernando Rubio}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Avoiding strategic behaviors in the egalitarian social welfare under public resources and non-additive utilities}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In multi-agent resource allocation systems, it is reasonable that the specific allocation of resources depends on the utility functions declared by the different agents. However, this can easily lead to strategic behaviors in which the agents involved are interested in lying, since such lies can bring them more profitable deals. In this paper we analyze the case of egalitarian social welfare, where the objective is to maximize the utility of the agent who receives the least utility. In this context, agents can obtain advantages by undervaluing their preferences. Thus, we will see how to discourage such lies even in the presence of public goods and non-additive utilities. Likewise, we will use genetic algorithms to show, through experimental results, the robustness of our proposal against lies.}, keywords = {Evolutionary computation, Robustness, Behavioral sciences, Resource management, Proposals, Genetic algorithms, Allocation of resources, genetic algorithms, social welfare, public resources}, doi = {doi:10.1109/CEC55065.2022.9870315}, notes = {Also known as \cite{9870315}}, ) @INPROCEEDINGS(Stevenson:2022:CEC, %xplor 24 Sep 2022 author = {Emma Stevenson and Riansares Martinez and Victor Rodriguez-Fernandez and David Camacho}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.}, keywords = {Deep learning, Space vehicles, Space missions, Time series analysis, Self-supervised learning, Transformers, Solar system, Deep Learning, Time Series, Transfomers, Self-supervised Learning, Asteroid Deflection, Planetary Defence}, doi = {doi:10.1109/CEC55065.2022.9870215}, notes = {Also known as \cite{9870215}}, ) @INPROCEEDINGS(Vieira:2022:CEC, %xplor 24 Sep 2022 author = {Miguel Vieira and Ricardo Faia and Fernando Lezama and Zita Vale}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Sensitivity Analysis of {PSO} Parameters Solving the {P2P} Electricity Market Problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Energy community markets have emerged to promote prosumers' active participation and empowerment in the electrical power system. These initiatives allow prosumers to transact electricity locally without an intermediary such as an aggregator. However, it is necessary to implement optimization methods that determine the best transactions within the energy community, obtaining the best solution under these models. Particle Swarm Optimization (PSO) fits this type of problem well because it allows reaching results in short optimization times. Furthermore, applying this metaheuristic to the problem is easy compared to other available optimization tools. In this work, we provide a sensitivity analysis of the impact of different parameters of PSO in solving an energy community market problem. As a result, the combination of parameters that lead to the best results is obtained, demonstrating the effectiveness of PSO solving different case studies.}, keywords = {Sensitivity analysis, Cogeneration, Metaheuristics, Optimization methods, Electricity supply industry, Generators, Power systems, Local electricity markets, Particle Swarm Optimization, Peer-to-Peer transactions, Sensitivity analysis, Swarm intelligence}, doi = {doi:10.1109/CEC55065.2022.9870290}, notes = {Also known as \cite{9870290}}, ) @INPROCEEDINGS(Duflo:2022:CEC, %xplor 24 Sep 2022 author = {Gabriel Duflo and Gregoire Danoy and El-Ghazali Talbi and Pascal Bouvry}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Generative Hyper-Heuristic based on Multi-Objective Reinforcement Learning: the UAV Swarm Use Case}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The interest in Unmanned Aerial Vehicles (UAVs) for civilian applications has seen a drastic increase in the past few years. Indeed, UAVs feature unique properties such as three-dimensional mobility and payload flexibility which provide unprecedented advantages when conducting missions like infrastructure inspection or search and rescue. However their current usage is mainly limited to a single operated or autonomous device which brings several limitations like its range of action and resilience. Using several UAVs as a swarm is one promising approach to address those limitations. However, manually designing globally efficient swarming approaches that solely rely on distributed behaviours is a complex task. The goal of this work is thus to automate the design of UAV swarming behaviours to tackle an area coverage problem. The first contribution of this work consists in modelling this problem as a multi-objective optimisation problem. The second contribution is a hyper-heuristic based on multi-objective reinforcement learning for generating distributed heuristics for that problem. Experimental results demonstrate the good stability of the generated heuristic on instances with different sizes and its capacity to well balance the multiple objectives of the optimisation problem.}, keywords = {Q-learning, Computational modeling, Evolutionary computation, Inspection, Autonomous aerial vehicles, Stability analysis, Task analysis, Hyper-Heuristic, Multi-Objective Reinforcement Learning, UAV Swarming}, doi = {doi:10.1109/CEC55065.2022.9870223}, notes = {Also known as \cite{9870223}}, ) @INPROCEEDINGS(Arpaci:2022:CEC, %xplor 24 Sep 2022 author = {Anil Arpaci and Jun Chen and John H. Drake and Tim Glover}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Sequence-based Selection Hyper-heuristics for Real-World Fibre Network Design Optimisation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In light of increased demand for streaming services, the need for more cost-effective network services is pressing. The telecommunication industry is facing tight budgets and severe competition. Therefore, reducing the cost of designing a fibre network via automation and optimisation has become critical. In order to automate and optimise network designs, British Telecom (BT) have developed a network design software, BT NetDesign, which includes a number of heuristics to search the design space of networks using a simulated annealing (SA) search strategy. Although NetDesign's current SA-based method is able to provide exploration and exploitation via different move heuristics, it cannot consistently reach the near-global optimum as the search space grows exponentially with the size of the network. To deal with larger networks, this study implements sequence-based hyper-heuristics utilising a hidden Markov model (HMM) with different acceptance strategies. The proposed methods have been rigorously analysed and compared using real-world network instances of different sizes. Results showed that HMM with a longer learning period and threshold acceptance strategy has promising ability to reach high quality solutions for large real-world problem instances.}, keywords = {Industries, Costs, Hidden Markov models, Simulated annealing, Pressing, Evolutionary computation, Search problems}, doi = {doi:10.1109/CEC55065.2022.9870334}, notes = {Also known as \cite{9870334}}, ) @INPROCEEDINGS(Lacerda:2022:CEC, %xplor 24 Sep 2022 author = {Eduardo Lacerda and Fernando Lezama and Joao Soares and Bruno Canizes and Zita Vale}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Metaheuristic Optimization Solving Demand Response Contract Markets with Network Validation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This article evaluates the performance of different metaheuristics (evolutionary algorithms) solving a cost mini-mization problem in demand response contract markets. The problem considers a contract market in which a distribution system operator (DSO) requests flexibility from aggregators with DR capabilities. We include a network validation approach in the evaluation of solutions, i.e., the DSO determines losses and voltage limit violations depending on the location of aggregators in the network. The validation of the network increases the complexity of the objective function since new network constraints are included in the formulation. Therefore, we advocate the use of metaheuristic optimization and a simulation procedure to overcome this issue. We compare different evolutionary algorithms, including the well-known differential evolution and other two more recent algorithms, the vortex search and the hybrid-adaptive differential evolution with decay function. Results demonstrate the effectiveness of these approaches in solving the proposed complex model under a realistic case study.}, keywords = {Uncertainty, Costs, Computational modeling, Metaheuristics, Evolutionary computation, Voltage, Linear programming}, doi = {doi:10.1109/CEC55065.2022.9870325}, notes = {Also known as \cite{9870325}}, ) @INPROCEEDINGS(Liu:2022:CEC, %xplor 24 Sep 2022 author = {Ying Ying Liu and Parimala Thulasiraman and Nelishia Pillay}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Bicriterion Coevolution for the Multi-objective Travelling Salesperson Problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The travelling salesperson problem is an NP-hard combinatorial optimization problem. In this paper, we consider the multi-objective travelling salesperson problem (MTSP), both static and dynamic, with conflicting objectives. NSGA-II and MOEA/D, two popular evolutionary multi-objective optimization algorithms suffer from loss of diversity and poor convergence when applied separately on MTSP. However, both these techniques have their individual strengths. NSGA-II maintains di-versity through non-dominated sorting and crowding distance selection. MOEA/D is good at exploring extreme points on the Pareto front with faster convergence. In this paper, we adopt the bicriterion framework that exploits the strengths of Pareto-Criterion (PC) and Non-Pareto Criterion (NPC) evolutionary populations. In this research, NSGA-II (PC) and MOEA/D (NPC) coevolve to compensate the diversity of each other. We further improve the convergence using local search and a hybrid of order crossover and inver-over operators. To our knowledge, this is the first work that combines NSGA-II and MOEA/D in a bicriterion framework for solving MTSP, both static and dynamic. We perform various experiments on different MTSP bench-mark datasets with and without traffic factors to study static and dynamic MTSP. Our proposed algorithm is compared against standard algorithms such as NSGA-II & III, MOEA/D, and a baseline divide and conquer coevolution technique using performance metrics such as inverted generational distance, hypervolume, and the spacing metric to concurrently quantify the convergence and diversity of our proposed algorithm. We also compare our results to datasets used in the literature and show that our proposed algorithm performs empirically better than compared algorithms.}, keywords = {Measurement, Heuristic algorithms, Sociology, Evolutionary computation, Search problems, Statistics, Optimization, multi-objective travelling salesperson prob-lem, dynamic multi-objective optimization, combinatorial multi-objective optimization, bicriterion coevolution, local search, ge-netic operator}, doi = {doi:10.1109/CEC55065.2022.9870282}, notes = {Also known as \cite{9870282}}, ) @INPROCEEDINGS(Dommaraju:2022:CEC, %xplor 24 Sep 2022 author = {Nivesh Dommaraju and Mariusz Bujny and Stefan Menzel and Markus Olhofer and Fabian Duddeck}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Cooperative Multi-objective Topology Optimization Using Clustering and Metamodeling}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Topology optimization optimizes material layout in a design space for a given objective, such as crash energy absorption, and a set of boundary conditions. In industrial applications, multi-objective topology optimization requires expensive simulations to evaluate the objectives and generate multiple Pareto-optimal solutions. So, it is more economical to identify preferred regions on the Pareto front and generate only the desired solutions. Clustering methods, a widely used subclass of machine learning methods, provide an unsupervised approach to summarize the dataset, which eases the identification of the preferred set of designs. However, generating solutions similar to the preferred designs based on different metrics is a challenging task. In this paper, we present an interactive method to generate designs similar to a preferred set using one of the state-of-the-art weighted-sum approaches called scaled energy weighting - hybrid cellular automata (SEW-HCA). To avoid unnecessary computations, metamodels are used to predict the desired weight vectors needed by SEW-HCA. We evaluate an application of our method for cooperative topology optimization using a cantilever multi-load-case problem and a crashworthiness optimization problem. Using the proposed method, we could successfully generate designs that are similar to preferred solutions based on geometry and performance. We believe that this is a crucial component that will improve the usefulness of multi-objective topology optimization in real-world applications.}, keywords = {Measurement, Geometry, Automata, Machine learning, Sampling methods, Computer crashes, Product design, multi-objective optimization, topology optimization, similarity measures, data mining, geometric processing}, doi = {doi:10.1109/CEC55065.2022.9870326}, notes = {Also known as \cite{9870326}}, ) @INPROCEEDINGS(Moravvej:2022:CEC, %xplor 24 Sep 2022 author = {Seyed Vahid Moravvej and Seyed Jalaleddin Mousavirad and Diego Oliva and Gerald Schaefer and Zahra Sobhaninia}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An Improved {DE} Algorithm to Optimise the Learning Process of a BERT-based Plagiarism Detection Model}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Plagiarism detection is a challenging task, aiming to identify similar items in two documents. In this paper, we present a novel approach to automatic plagiarism detection that combines BERT (bidirectional encoder representations from transformers) word embedding, attention mechanism-based long short-term memory (LSTM) networks, and an improved differential evolution (DE) algorithm for weight initialisation. BERT is used to pretrain deep bidirectional representations in all layers, while the pre-trained BERT model can be fine-tuned with only one extra output layer without significant changes in architecture. Deep learning algorithms often use the random weighting method for initialisation, followed by gradient-based optimisation algorithms such as back-propagation for training, making them susceptible to getting trapped in local optima. To address this, population- based metaheuristic algorithms such as DE can be used. We propose an improved DE algorithm with a clustering-based mutation operator, where first a winning cluster of candidate solutions is identified and a new updating strategy is then applied to include new candidate solutions in the current population. The proposed DE algorithm is used in LSTM, attention mechanism, and feed- forward neural networks to yield the initial seeds for subsequent gradient-based optimisation. We compare our proposed model with conventional and population-based approaches on three datasets (SNLI, MSRP and SemEval2014) and demonstrate it to give superior plagiarism detection performance.}, keywords = {Deep learning, Training, Plagiarism, Bit error rate, Sociology, Neural networks, Clustering algorithms, Plagiarism detection, BERT, LSTM, attention mechanism, differential evolution}, doi = {doi:10.1109/CEC55065.2022.9870280}, notes = {Also known as \cite{9870280}}, ) @INPROCEEDINGS(Godoy:2022:CEC, %xplor 24 Sep 2022 author = {Aitor Godoy and Ismael Rodriguez and Fernando Rubio}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={On the hardness of finding good pacts}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Reaching agreements is part of the life of any human group, but it is especially important in the context of political relations. In parliamentary systems, when no party has an absolute majority, it is necessary to establish pacts with other parties to carry out as many laws as possible that fit with our ideology. However, finding the best possible deals is not an easy task. In fact, in this work we not only show that it is an NP-complete problem, but also that it is impossible to guarantee a good approximation ratio in polynomial time. Even so, we show that it is possible to use genetic algorithms to obtain reasonably satisfactory pacts, and we illustrate it for a specific case study of the Spanish parliament.}, keywords = {Evolutionary computation, Approximation algorithms, NP-complete problem, Data mining, Task analysis, Genetic algorithms, Complexity, Genetic algorithms, Pacting, In-naproximability, Political problems}, doi = {doi:10.1109/CEC55065.2022.9870393}, notes = {Also known as \cite{9870393}}, ) @INPROCEEDINGS(Kornaeva:2022:CEC, %xplor 24 Sep 2022 author = {Elena Kornaeva and Alexey Kornaev and Alexander Fetisov and Ivan Stebakov and Bulat Ibragimov}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Physics-based loss and machine learning approach in application to non-Newtonian fluids flow modeling}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The variational approach of finding the extremum of an objective functional is an alternative approach to the solution of partial differential equations in mechanics of continua. The great challenge in the calculus of variations direct methods is to find a set of functions that will be able to approximate the solution accurately enough. Artificial neural networks are a powerful tool for approximation, and the physics-based functional can be the natural loss for a machine learning method. In this paper, we focus on the loss that may take non-linear fluid properties and mass forces into account. We modified the energy-based variational principle and determined the constraints on its unknown functions that implement boundary conditions. We explored artificial neural networks as an option for loss minimization and the approximation of the unknown functions. We compared the obtained results with the known solutions. The proposed method allows modeling non-Newtonian fluids flow including blood, synthetic oils, paints, plastic, bulk materials, and even rheomagnetic fluids. The fluids flow velocity approximation error was up to percent in comparison with the analytical and numerical solutions.}, keywords = {Solid modeling, Fluids, Three-dimensional displays, Fluid flow, Machine learning, Minimization, Boundary conditions, physics-based machine learning, differentiable physics, loss, multilayer perceptron, convolutional neural network, continuum mechanics, calculus of variations, variational principle}, doi = {doi:10.1109/CEC55065.2022.9870411}, notes = {Also known as \cite{9870411}}, ) @INPROCEEDINGS(Pochmann:2022:CEC, %xplor 24 Sep 2022 author = {Vitor O. Pochmann and Fernando J. {Von Zuben}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-Objective Bilevel Recommender System for Food Diets}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This work aimed to develop a personalized multi-objective recommender system for food diets, which seeks to suggest to the user a diverse list of four meals a day (breakfast, lunch, snack, dinner). The efficient solutions are capable of simultaneously meeting a list of nutritional specifications and also minimizing both the concentration in a specific meal or food item and the total cost of acquisition and preparation. Efficient solutions are sought from the joint use of the NSGA-II and Gurobi optimization packages, after formulating the diet problem as a bilevel optimization: a combinatorial and multi-objective problem at the upper level - which food items from each category (the available food categories and the categories allocated to each of the four meals are defined in advance) should make up the diet - and a mathematical programming problem at the lower level - what is the optimal amount of the selected food items to compose the diet, given nutritional constraints. As Gurobi does not operate directly with a multi-objective optimization perspective, its lower-level objective function involves maximizing the total energy of the daily diet. Experimental results, considering fictitious food costs, show that NSGA-II and Gurobi operate in synergy, providing a diverse list of menus for the four daily meals, thus making a valuable approximation of the Pareto frontier. The distinctive aspect of this multi-objective bilevel solution to the diet problem, then, resides in the supply of diverse and, at the same time, efficient candidate solutions, in the sense of achieving the Pareto frontier and being scattered along its extension.}, keywords = {Costs, Evolutionary computation, Linear programming, Optimization, Recommender systems, Mathematical programming, Food diet, bilevel optimization, multi-objective optimization, mathematical programming, diversity of efficient menus}, doi = {doi:10.1109/CEC55065.2022.9870408}, notes = {Also known as \cite{9870408}}, ) @INPROCEEDINGS(Zoppi:2022:CEC, %xplor 24 Sep 2022 author = {Giacomo Zoppi and Leonardo Vanneschi and Mario Giacobini}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Reducing the Number of Training Cases in Genetic Programming}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In the field of Machine Learning, one of the most common and discussed questions is how to choose an adequate number of data observations, in order to train our models satisfactorily. In other words, find what is the right amount of data needed to create a model, that is neither underfitted nor overfitted, but instead is able to achieve a reasonable generalization ability. The problem grows in importance when we consider Genetic Programming, where fitness evaluation is often rather slow. Therefore, finding the minimum amount of data that enables us to discover the solution to a given problem could bring significant benefits. Using the notion of entropy in a dataset, we seek to understand the information gain obtainable from each additional data point. We then look for the smallest percentage of data that corresponds to enough information to yield satisfactory results. We present, as a first step, an example derived from the state of art. Then, we question a relevant part of our procedure and introduce two case studies to experimentally validate our theoretical hypothesis.}, keywords = {Training, Boolean functions, Genetic programming, Machine learning, Evolutionary computation, Benchmark testing, Data models}, doi = {doi:10.1109/CEC55065.2022.9870327}, notes = {Also known as \cite{9870327}}, ) @INPROCEEDINGS(Gomez:2022:CEC, %xplor 24 Sep 2022 author = {Jonatan Gomez and Elizabeth Leon}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Gabo: Gene Analysis Bitstring Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper analyzes bitstring functions, character-izes genes according to their contribution to the genome's fitness, and proposes an optimization algorithm (G ABO) that uses this characterization for directing the optimization process. We define a gene's contribution as the difference between the genome's fitness when the gene takes a value of 1 and its fitness when the gene takes a value of 0. We characterize a gene as intron-like if it does not contribute to the genome's fitness (zero difference) and as separable-like if its contribution to the fitness of both the genome and genome's complement is the same. Gabo divides genes into two groups coding-like and intron-like genes. Then it searches for an optimal solution by reducing intron-like genes (IOSA) and analyzing coding-like genes (COSA). G Aborepeats these two steps while there are intron-like genes, not all genes are separable-like, and function evaluations are available. We test the performance of Gabo on well-known binary-encoding functions and a function that we define as the mix of them. Our results indicate that G Aboproduces the optimal or near to the optimal solution on the tested functions expending a reduced number of function evaluations and outperforming well-established optimization algorithms.}, keywords = {Genomics, Evolutionary computation, Bioinformatics, Optimization}, doi = {doi:10.1109/CEC55065.2022.9870237}, notes = {Also known as \cite{9870237}}, ) @INPROCEEDINGS(De-La-Cruz:2022:CEC, %xplor 24 Sep 2022 author = {Marina {De La Cruz} and Carlos Cervigon and Jorge Alvarado and Marta Botella-Serrano and J.Ignacio Hidalgo}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving Classification Rules for Predicting Hypoglycemia Events}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={People with diabetes have to properly manage their blood glucose levels in order to avoid acute complications. This is a difficult task and an accurate and timely prediction may be of vital importance, specially of extreme values. Perhaps one of the main concerns of people with diabetes is to suffer an hypoglycemia (low value) event and moreover, that the event will be prolonged in time. It is crucial to predict events of hyperglycemia (high value) and hypoglycemia that may cause health damages in the short term and potential permanent damages in the long term. The aim of this paper is to describe our research on predicting hypoglycemia events using Dynamic structured Grammatical Evolution. Our proposal gives white box models induced by a grammar based on if-then-else conditions. We trained and tested our system with real data collected from 5 different diabetic patients, producing 30 minutes predictions with encouraging results.}, keywords = {Wearable Health Monitoring Systems, Predictive models, Prediction algorithms, Diabetes, Glucose, Grammar, Proposals, Diabetes, Hypoglycemia prediction, Rule System, Structured Grammatical Evolution}, doi = {doi:10.1109/CEC55065.2022.9870380}, notes = {Also known as \cite{9870380}}, ) @INPROCEEDINGS(Tao:2022:CEC, %xplor 24 Sep 2022 author = {Ning Tao and Anthony Ventresque and Takfarinas Saber}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective Grammar-guided Genetic Programming with Code Similarity Measurement for Program Synthesis}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Grammar-Guided Genetic Programming (G3P) is widely recognised as one of the most successful approaches for program synthesis, i.e., the task of automatically discovering an executable piece of code given user intent. G3P has been shown capable of successfully evolving programs in arbitrary languages that solve several program synthesis problems based only on a set of input/output examples. Despite its success, the restriction on the evolutionary system to only leverage input/output error rate during its assessment of the programs it derives limits its scalabil-ity to larger and more complex program synthesis problems. With the growing number and size of open software repositories and generative artificial intelligence approaches, there is a sizeable and growing number of approaches for retrieving/generating source code (potentially several partial snippets) based on textual problem descriptions. Therefore, it is now, more than ever, time to introduce G3P to other means of user intent (particularly textual problem descriptions). In this paper, we would like to assess the potential for G3P to evolve programs based on their similarity to particular target codes of interest (obtained using some code retrieval/generative approach). Through our experimental evaluation on a well-known program synthesis benchmark, we have shown that G3P successfully manages to evolve some of the desired programs with all four considered similarity measures. However, in its default configuration, G3P is not as successful with similarity measures as it is with the classical input/output error rate when solving program synthesis problems. Therefore, we propose a novel multi-objective G3P approach that combines the similarity to the target program and the traditional input/output error rate. Our experiments show that compared to the error-based G3P, the multi-objective G3P approach could improve the success rate of specific problems and has great potential to improve on the traditional G3P system.}, keywords = {Codes, Error analysis, Measurement uncertainty, Genetic programming, Evolutionary computation, Benchmark testing, Software, Program Synthesis, Grammar-Guided Genetic Programming, Code Similarity, Multi-Objective Optimization}, doi = {doi:10.1109/CEC55065.2022.9870312}, notes = {Also known as \cite{9870312}}, ) @INPROCEEDINGS(Bulanova:2022:CEC, %xplor 24 Sep 2022 author = {Nina Bulanova and Arina Buzdalova and Carola Doerr}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Fast Re-Optimization of {LeadingOnes} with Frequent Changes}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In real-world optimization scenarios, the problem instance that we are asked to solve may change during the optimization process, e.g., when new information becomes available or when the environmental conditions change. In such situations, one could hope to achieve reasonable performance by continuing the search from the best solution found for the original problem. Likewise, one may hope that when solving several problem instances that are similar to each other, it can be beneficial to "warm-start" the optimization process of the second instance by the best solution found for the first. However, it was shown in [Doerr et al., GECCO 2019] that even when initialized with structurally good solutions, evolutionary algorithms can have a tendency to replace these good solutions by structurally worse ones, resulting in optimization times that have no advantage over the same algorithms started from scratch. Doerr et al. also proposed a diversity mechanism to overcome this problem. Their approach balances greedy search around a best-so-far solution for the current problem with search in the neighborhood around the best-found solution for the previous instance. In this work, we first show that the re-optimization approach suggested by Doerr et al. reaches a limit when the problem instances are prone to more frequent changes. More precisely, we show that they get stuck on the dynamic LeadingOnes problem in which the target string changes periodically. We then propose a modification of their algorithm which interpolates between greedy search around the previous-best and the current-best solution. We empirically evaluate our smoothed re-optimization algorithm on LeadingOnes instances with various frequencies of change and with different perturbation factors and show that it outperforms both a fully restarted ($1+1$) Evolutionary Algorithm and the re-optimization approach by Doerr et al.}, keywords = {Perturbation methods, Heuristic algorithms, Evolutionary computation, Switches, Search problems, Dynamic scheduling, Routing}, doi = {doi:10.1109/CEC55065.2022.9870400}, notes = {Also known as \cite{9870400}}, ) @INPROCEEDINGS(Abdel-Nabi:2022:CEC, %xplor 24 Sep 2022 author = {Heba Abdel-Nabi and Mostafa Ali and Mohammad Daoud and Rami Alazrai and Arafat Awajan and Robert Reynolds and Ponnuthurai N. Suganthan}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An Enhanced Multi-Phase Stochastic Differential Evolution Framework for Numerical Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Real-life problems can be expressed as optimization problems. These problems pose a challenge for researchers to design efficient algorithms that are capable of finding optimal solutions with the least budget. Stochastic Fractal Search (SFS) proved its powerfulness as a metaheuristic algorithm through the large research body that used it to optimize different industrial and engineering tasks. Nevertheless, as with any meta-heuristic algorithm and according to the "No Free Lunch" theorem, SFS may suffer from immature convergence and local minima trap. Thus, to address these issues, a popular Differential Evolution variant called Success-History based Adaptive Differential Evolution (SHADE) is used to enhance SFS performance in a unique three-phase hybrid framework. Moreover, a local search is also incorporated into the proposed framework to refine the quality of the generated solution and accelerate the hybrid algorithm convergence speed. The proposed hybrid algorithm, namely eMpSDE, is tested against a diverse set of varying complexity optimization problems, consisting of well-known standard unconstrained unimodal and multimodal test functions and some constrained engineering design problems. Then, a comparative analysis of the performance of the proposed hybrid algorithm is carried out with the recent state of art algorithms to validate its competitivity.}, keywords = {Statistical analysis, Welding, Scalability, Metaheuristics, Search problems, Fractals, Task analysis, Optimization, Evolutionary Computation, Hybrid Algorithm, SHADE, Stochastic Fractal Search}, doi = {doi:10.1109/CEC55065.2022.9870438}, notes = {Also known as \cite{9870438}}, ) @INPROCEEDINGS(Habbab:2022:CEC, %xplor 24 Sep 2022 author = {Fatim Z. Habbab and Michael Kampouridis}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimizing Mixed-Asset Portfolios With Real Estate: Why Price Predictions?}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The main purpose of portfolio optimization is to reduce the risk, and/or maximize the return of a group of investments. Most of the works that have been done on port-folio optimization are based on the Modern Portfolio Theory introduced by Markowitz in 1959. Some of them have employed price predictions to compute optimal asset weights. It has been demonstrated that using price predictions, instead of historical data, might improve portfolio performance under a risk-adjusted perspective. However, contributions in the field mainly focused on stocks, while little attention has been given on multi-asset portfolios including real estate. In this paper, we fill this gap by running a genetic algorithm on 456 portfolios to demonstrate the added value of including price predictions in our asset allocation problem. To investigate this, we compare the theoretical case of having a perfect foresight, where the predicted price $p_{t}$ is exactly the same as the expected price pt; under this case, the portfolio optimization task takes place in the test set (since we have assumed a perfect price prediction). We compare the results under perfect foresight with results derived from portfolio optimization that only took place in the training set, and the weights were then directly applied to the test set. Our goal is to demonstrate the theoretical advantages of using price predictions on mixed-asset portfolios that include real estate. Our results show that there can be significant improvements (up to 45percent) in sharpe ratio, rate of return, and risk, when using price predictions instead of a historical prices based portfolio.}, keywords = {Training, Machine learning algorithms, Evolutionary computation, Resource management, Task analysis, Portfolios, Optimization, genetic algorithm, mixed-asset portfolio, perfect foresight, portfolio optimization, risk-adjusted return}, doi = {doi:10.1109/CEC55065.2022.9870236}, notes = {Also known as \cite{9870236}}, ) @INPROCEEDINGS(De-Vega:2022:CEC, %xplor 24 Sep 2022 author = {Francisco Fernandez {De Vega} and Jorge Alvarado and Juan Villegas Cortez}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Optical Music recognition and Deep Learning: An application to 4-part harmony}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Optical Music Recognition (OMR) applied to hand-written scores is widely recognized as a hard real-world problem. The number of different symbols that must be recognized in a score, such as the key, time signatures, tempo, dynamics, notes, alterations, duration, etc, as well as the different meanings some symbols may embody depending on the position in the score, such as a quarter that may mean notes C, D, E, ..., makes OMR much harder challenge than Optical Character Recognition (OCR), particularly when dealing with handwritten scores for Computational Intelligence (CI) methods. This paper addresses this hard problem using deep learning based approaches, specif-ically Mask R-CNN, in a specific context: music students that write their scores in a ruled paper notebook when learning 4-part harmony. Preliminary results show that high accuracy levels are obtained, both during training+validation and also during tests, and this allows us to foresee new tools for students that could be combined with available CI methods for 4-part harmony learning.}, keywords = {Deep learning, Training, Handwriting recognition, Image segmentation, Optical character recognition, Symbols, Evolutionary computation, OMR, Deep Learning, Image recognition}, doi = {doi:10.1109/CEC55065.2022.9870357}, notes = {Also known as \cite{9870357}}, ) @INPROCEEDINGS(Maslen:2022:CEC, %xplor 24 Sep 2022 author = {Jordan Maslen and Brian J. Ross}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Mixed Media in Evolutionary Art}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Mixed media in the real world involves the creation of works of art that creatively combine a variety of media on the canvas, for example, watercolour, acrylic paint, and photographs. We present an evolutionary art system that implements a digital version of mixed media. A genetic programming system uses a language that renders different digital effects on a canvas. Each rendered effect takes the form of an "art object", and the tree defines a s et o fa rt o bjects that together comprise a final rendered image. Available effects include procedural images (textures), image filters, and bitmaps. A n art o bject is rendered onto the canvas via a pre-defined mask shape, which c an range from simple geometric shapes such as circles or squares, to com-plex paintbrush strokes and paint splatters. Fitness evaluation measures the pixel-by-pixel colour distance between a rendered canvas and an input target image, which acts as a compositional guide for rendered images. Various runs of the system have produced an interesting variety of stylized, mixed-effect results, often appearing as abstract "glitchy" interpretations of target images.}, keywords = {Image texture, Histograms, Art, Image color analysis, Shape, Graphics processing units, Media, genetic programming, evolutionary art, mixed media}, doi = {doi:10.1109/CEC55065.2022.9870271}, notes = {Also known as \cite{9870271}}, ) @INPROCEEDINGS(Cruz-Duarte:2022:CEC, %xplor 24 Sep 2022 author = {Jorge M. Cruz-Duarte and Ivan Amaya and Jose Carlos Ortiz-Bayliss and Nelishia Pillay}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Transfer Learning Hyper-heuristic Approach for Automatic Tailoring of Unfolded Population-based Metaheuristics}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={It is no secret that optimisation is a popular topic in any practical engineering application. Similarly, Metaheuristics (MHs) are a fairly standard approach for solving optimisation problems due to their success, flexibility, and simplicity. However, it is seldom easy to find a solver from the overpopulation of metaheuristics that adequately deals with a given problem. For that reason, the solver selection is even considered an additional problem in many optimisation scenarios. This work investigates the Metaheuristic Composition Optimisation Problem, which involves designing heuristic-based procedures that solve continuous optimisation problems. Therefore, we propose two novel and still simple methodologies based on transfer learning to facilitate the automatic generation of population-based and metaphor-less MHs by using search operators from the literature. To represent these solvers, we adopt our previously proposed unfolded MH model. The first strategy deals with the problem dynamically, building the sequence while solving the low-level problem. In contrast, the second one does it statically by generating the whole candidate sequence before implementing it. Results provide us with information to prove the feasibility of these approaches via experiments using 32 problems with four different characteristic groups and four dimensionalities and varying the number of agents (30, 50, and 100) employed by the search operators. We also remark that one can compare these two methodologies on performance, but we emphasise their potential usage depending on the general application environment.}, keywords = {Metaheuristics, Transfer learning, Sociology, Buildings, Evolutionary computation, Search problems, Statistics, Hyper-heuristic, Transfer Learning, Search Operators, Optimisation, Evolutionary Computation}, doi = {doi:10.1109/CEC55065.2022.9870426}, notes = {Also known as \cite{9870426}}, ) @INPROCEEDINGS(Batista:2022:CEC, %xplor 24 Sep 2022 author = {Joao E. Batista and Sara Silva}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Comparative study of classifier performance using automatic feature construction by M3GP}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The M3GP algorithm, originally designed to per-form multiclass classification with genetic programming, is also a powerful feature construction method. Here we explore its ability to evolve hyper-features that are tailored not only to the problem to be solved, but also to the learning algorithm that is used to solve it. We pair M3GP with six different machine learning algorithms and study its performance in eight classification problems from different scientific domains, with substantial variety in the number of classes, features and samples. The results show that automatic feature construction with M3GP, when compared to using the standalone classifiers without feature construction, achieves statistically significant improvements in the majority of the test cases, sometimes by a very large margin, while degrading the weighted f-measure in only one out of 48 cases. We observe the differences in the number and size of the hyper-features evolved for each case, hypothesising that the simpler the classifier, the larger the amount of problem complexity is being captured in the hyper-features. Our results also reveal that the M3GP algorithm can be improved, both in execution time and in model quality, by replacing its default classifier with support vector machines or random forest classifiers.}, keywords = {Support vector machines, Machine learning algorithms, Computational modeling, Genetic programming, Evolutionary computation, Classification algorithms, Complexity theory, Feature Construction, Genetic Programming, Multiclass Classification}, doi = {doi:10.1109/CEC55065.2022.9870343}, notes = {Also known as \cite{9870343}}, ) @INPROCEEDINGS(Tapia-Avitia:2022:CEC, %xplor 24 Sep 2022 author = {Jose M. Tapia-Avitia and Jorge M. Cruz-Duarte and Ivan Amaya and Jose Carlos Ortiz-Bayliss and Hugo Terashima-Marin and Nelishia Pillay}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Metaheuristics (MHs) are proven powerful algorithms for solving non-linear optimisation problems over discrete, continuous, or mixed domains. Applications have ranged from basic sciences to applied technologies. Nowadays, the literature contains plenty of MHs based on exceptional ideas, but often, they are just recombining elements from other techniques. An alternative approach is to follow a standard model that customises population-based MHs, utilising simple heuristics extracted from well-known MHs. Different approaches have explored the combination of such simple heuristics, generating excellent results compared to the generic MHs. Nevertheless, they present limitations due to the nature of the metaheuristic used to study the heuristic space. This work investigates a field of action for implementing a model that takes advantage of previously modified MHs by learning how to boost the performance of the tailoring process. Following this reasoning, we propose a hyper-heuristic model based on Artificial Neural Networks (ANNs) trained with processed sequences of heuristics to identify patterns that one can use to generate better MHs. We prove the feasibility of this model by comparing the results against generic MHs and other approaches that tailor unfolded MHs. Our results evidenced that the proposed model outperformed an average of 84percent of all scenarios; in particular, 89percent of basic and 77percent of unfolded approaches. Plus, we highlight the configurable capability of the proposed model, as it shows to be exceptionally versatile in regards to the computational budget, generating good results even with limited resources.}, keywords = {Analytical models, Computational modeling, Metaheuristics, Training data, Artificial neural networks, Feature extraction, Transformers, Hyper-heuristic, Artificial Neural Networks, Search Operators, Optimisation, Evolutionary Computation}, doi = {doi:10.1109/CEC55065.2022.9870275}, notes = {Also known as \cite{9870275}}, ) @INPROCEEDINGS(Mavrovouniotis:2022:CEC, %xplor 24 Sep 2022 author = {Michalis Mavrovouniotis and Changhe Li and Georgios Ellinas and Marios Polycarpou}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Solving the Electric Capacitated Vehicle Routing Problem with Cargo Weight}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Electric vehicle routing problems are challenging variations of the traditional vehicle routing problem which incorporate the possibility of electric vehicle (EV) recharging at any station, while satisfying the delivery demands of customers. This work addresses the recently formulated capacitated vehicle routing problem (E-CVRP) with variable energy consumption rate. In particular, the cargo weight, which is one of the main factors affecting the energy consumption rate of EVs, is considered (i.e., the heavier the EV the higher the rate). As a solution method, an ant colony optimization algorithm with a local search heuristic is developed. Experiments are conducted on a recently generated benchmark set of E-CVRP instances demonstrating that the performance of the proposed technique improves on the best known so far solutions.}, keywords = {Energy consumption, Ant colony optimization, Heuristic algorithms, Vehicle routing, Evolutionary computation, Benchmark testing, Electric vehicles, Electric vehicle, capacitated vehicle routing problem, ant colony optimization}, doi = {doi:10.1109/CEC55065.2022.9870383}, notes = {Also known as \cite{9870383}}, ) @INPROCEEDINGS(Wang:2022:CEC, %xplor 24 Sep 2022 author = {Yirui Wang and Massimiliano Vasile}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Intelligent Decision Support System for Planetary Defense under Mixed Aleatory/Epistemic Uncertainties}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper studies the application of Machine Learning techniques in Planetary Defense. To quickly respond to an asteroid impact scenario, an Intelligent Decision Support System is proposed to automatically decide if a deflection mission is necessary, and then select the most effective deflection strategy. This system consists of two sub-systems: the first one is named as Asteroid Impact Scenarios Identifier, and the second one is named as Asteroid Deflection Strategies Selector. The input to the Asteroid Impact Scenarios Identifier is the warning time, the orbital parameters and the diameter of the asteroid and the corresponding uncertainties. According to the Probability of Collision and the corresponding confidence, the output is the decision of action: the deflection is needed, no deflection is needed, or more measurements need to be obtained before making any decision. If the deflection is needed, the Asteroid Deflection Strategies Selector is activated to output the most efficient deflection strategy that offers the highest probability of success. The training dataset is produced by generating thousands of virtual impact scenarios, sampled from the real distribution of Near-Earth Objects. A robust optimization is performed, under mixed aleatory/epistemic uncertainties, with five different deflection strategies (Nuclear Explosion Device, Kinetic Impactor, Laser Ablation, Gravity Tractor and Ion Beam Shepherd). The robust performance indices are considered as the deflection effectiveness, which is quantified by the change of impact probability pre and post deflection. We demonstrate the capabilities of Random Forest, Deep Neural Networks and Convolutional Neural Networks at classifying impact scenarios and deflection strategies. Simulation results suggest that the proposed system can quickly provide decisions to respond to an asteroid impact scenario. Once trained, the Intelligent Decision Support System, does not require re-running expensive simulations and is, therefore, suitable for the rapid prescreening deflection options.}, keywords = {Decision support systems, Performance evaluation, Deep learning, Training, Uncertainty, Simulation, Neural networks, Robust Optimisation, Machine Learning, Aster-oid Deflection, Epistemic Uncertainty}, doi = {doi:10.1109/CEC55065.2022.9870244}, notes = {Also known as \cite{9870244}}, ) @INPROCEEDINGS(Delgado-Enales:2022:CEC, %xplor 24 Sep 2022 author = {Inigo Delgado-Enales and Patricia Molina-Costa and Eneko Osaba and Silvia Urra-Uriarte and Javier {Del Ser}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Improving the Urban Accessibility of Older Pedestrians using Multi-objective Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Many countries around the world have witnessed the progressive ageing of their population, giving rise to a global concern to respond to the needs that this process will create. Besides the changes in the productive schemes and the evolution of the healthcare resources to new models, the accessibility of pedestrians belonging to this age range is grasping an increasing interest in urban planning processes. This work presents pre-liminary results of a framework that combines graph modeling and meta-heuristic optimization to inform decision makers in urban planning when deciding how to regenerate urban spaces taking into account pedestrian accessibility for the older people in urban areas with difficult orography. The goal of the framework is to decide where to deploy urban elements (mechanical ramps, escalators and lifts), so that an indirect measure of accessibility is improved while also accounting for the economical investment of the installation. We exploit the versatility of multi-objective evolutionary algorithms to tackle the underlying optimization problem. Experimental results of a case study located in the city of Santander (Spain) show that the proposed framework can support urban planners when making decisions regarding the accessibility of the public space.}, keywords = {Urban planning, Sociology, Metaheuristics, Evolutionary computation, Medical services, Grasping, Mechanical variables measurement, Urban planning, pedestrian accessibility, com-binatorial optimization, multi-objective evolutionary algorithms}, doi = {doi:10.1109/CEC55065.2022.9870432}, notes = {Also known as \cite{9870432}}, ) @INPROCEEDINGS(Takagi:2022:CEC, %xplor 24 Sep 2022 author = {Tomoaki Takagi and Keiki Takadama and Hiroyuki Sato}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Supervised Multi-Objective Optimization Algorithm Using Estimation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This work proposes a supervised multi-objective optimization algorithm that assumes the existence of non-dominated solutions that serve as supervised data. In an expensive multi-objective optimization problem, it is required to obtain a solution set that approximates the Pareto front with an extremely small number of function evaluations. We often know some good solutions in advance when dealing with optimization problems. In this case, instead of generating solutions from scratch, generating solutions from known good solutions can be a shortcut for optimization. The proposed method estimates the Pareto front and Pareto set using the response surface methodology with existing non-dominated solutions as the supervised data. The proposed method selects a subset of objective vectors on the estimated Pareto front and obtains the subset as the solution set. Experimental results using DTLZ and WFG test suites show that the proposed method works well even with only ten non-dominated solutions and 150 function evaluations.}, keywords = {Neural networks, Estimation, Evolutionary computation, Approximation algorithms, Response surface methodology, Optimization, Expensive multi-objective optimization, subset selection, Pareto front estimation, Pareto set estimation, radial basis neural network}, doi = {doi:10.1109/CEC55065.2022.9870375}, notes = {Also known as \cite{9870375}}, ) @INPROCEEDINGS(Zhu:2022:CEC, %xplor 24 Sep 2022 author = {Jian Zhu and Jianhua Liu and Zihang Wang and Yuxiang Chen}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Restructuring Particle Swarm Optimization algorithm based on linear system theory}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The original Particle Swarm Optimization (PSO) used two formulas to describe updating of particle's position and velocity, respectively, based on simulating the foraging behavior of bird swarm. The general improving methods on PSO are to adjust and optimize its parameters or combine new learning strategy to update velocity formula for the better performance. But these methods lack of theoretical analysis and make the algorithm more complex. This paper proposes a new formulation to restructure the particles' position updating behaviors based on linear system theory, and obtain a Restructuring PSO algorithm (RPSO). Compared with the conventional PSO algorithm, RPSO only uses one particle position updating formula, without velocity updating formula, and takes fewer parameters. In order to verify the effectiveness of RPSO, experiments on the CEC 2013 benchmark functions have been conducted to compare with four algorithms, and the final results show that proposed algorithm has a certain degree of competition.}, keywords = {Linear systems, Benchmark testing, Birds, Behavioral sciences, Particle swarm optimization, PSO, Restructuring PSO, Linear system theory}, doi = {doi:10.1109/CEC55065.2022.9870261}, notes = {Also known as \cite{9870261}}, ) @INPROCEEDINGS(Garbelini:2022:CEC, %xplor 24 Sep 2022 author = {Jader M. Caldonazzo Garbelini and Danilo Sipoli Sanches and Aurora Trinidad Ramirez Pozo}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Expectation Maximization based algorithm applied to {DNA} sequence motif finder}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Finding transcription factor binding sites plays an important role inside bioinformatics. Its correct identification in the promoter regions of co-expressed genes is a crucial step for understanding gene expression mechanisms and creating new drugs and vaccines. The problem of finding motifs consists in seeking conserved patterns in biological datasets of sequences, through using unsupervised learning algorithms. This problem is considered one of the open problems of computational biology, which in its simplest formulation has been proven to be np-hard. Moreover, heuristics and meta-heuristics algorithms have been shown to be very promising in solving combinatorial problems with very large search spaces. In this paper we propose a new algorithm called Biomapp (Biological Motif Application) based on canonical Expectation Maximization that uses the Kullback-Leibler divergence to re-estimate the parameters of statistical model. Furthermore, the algorithm is embedded in an Iterated Local Search, as the local search step and then, we use a hierarchical perturbation operator in order to escape from local optima. The results obtained by this new approach were compared with the state-of-the-art algorithm MEME (Multiple EM Motif Elicitation) showing that Biomapp outperformed this classical technique in several datasets.}, keywords = {Measurement, Monte Carlo methods, Biological system modeling, Perturbation methods, Computational modeling, Metaheuristics, Search problems, biological motifs, expectation maximization, kullback-leibler divergence, iterated local search}, doi = {doi:10.1109/CEC55065.2022.9870303}, notes = {Also known as \cite{9870303}}, ) @INPROCEEDINGS(Tahernezhad-Javazm:2022:CEC, %xplor 24 Sep 2022 author = {Farajollah Tahernezhad-Javazm and Debbie Rankin and Damien Coyle}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={R2-{HMEWO:} Hybrid multi-objective evolutionary algorithm based on the Equilibrium Optimizer and Whale Optimization Algorithm}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Multi-objective evolutionary algorithms can be categorized into three basic groups: domination-based, decomposition-based, and indicator-based algorithms. Hybrid multi-objective evolutionary algorithms, which combine algorithms from these groups, are gaining increased popularity in recent years. This is because hybrid algorithms can compensate for the drawbacks of the basic algorithms by adding different operators and structures that complement each other. This paper introduces a hybrid-multi objective evolutionary algorithm (R2-HMEWO) that applies hybridization in the form of structure and operators. R2-HMEWO is based on the whale optimization algorithm (WOA) and equilibrium optimizer (EO). Elite individuals of WOA and EO are selected from a repository based on the R2-indicator and shifted density estimation-based method. In order to improve solutions' diversity, a reference points method is devised to select next-generation individuals. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems (ZDT, DTLZ, and CEC009) and compared with six state-of-the-art (SOTA) algorithms (NSGA-III, NSGA-II, MOEA/D, MOMBI-II, MOEA/IGD-NS, and dMOPSO). Based on the inverted generational distance (IGD) metric (mean of 25 independent runs), R2-HMEWO outperformed other algorithms on 14 out of 19 test problems and revealed a highly competitive performance on the other test problems. Also, R2-HMEWO performed statistically significant better than MOEA/D and dMOPSO in 15/19 and 14/19 test problems, respectively ($p < 0.05$), and reached significant performance in 4 test problems (from ZDT and CEC09) compared to other algorithms.}, keywords = {Measurement, Algorithms, Estimation, Evolutionary computation, Benchmark testing, Optimization, Next generation networking, evolutionary algorithm, multi-objective optimization, whale optimization algorithm, equilibrium optimizer, reference directions, R2 indicator, shifted density estimation}, doi = {doi:10.1109/CEC55065.2022.9870371}, notes = {Also known as \cite{9870371}}, ) @INPROCEEDINGS(Miranda-Burgos:2022:CEC, %xplor 24 Sep 2022 author = {Victoria Miranda-Burgos and Nicolas Rojas-Morales}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Opposition-Inspired Strategies for Tabu Search approaches proposed for Knapsack Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The family of Knapsack Problems (KP) has been relevant in many works and studies as their use in modeling, simplifying complex problems or decision-making processes. Because of its importance, several metaheuristic algorithms have been designed or evaluated using this type of problem. In some variants of the KP, Tabu Search approaches are competitive or part of the state-of-the-art. This work proposes opposition-inspired strategies to improve the diversification of Tabu Search (TS) algorithms proposed for solving KPs. We use the well-known TSTS algorithm to evaluate our strategies, designed for solving the Multidemand Multidimensional Knapsack Problem. Results show that the usage of our opposite strategies allow the target algorithm to improve its performance in several benchmark instances.}, keywords = {Metaheuristics, Decision making, Evolutionary computation, Benchmark testing, Search problems, Trajectory, Behavioral sciences, Opposition-Inspired Learning, Tabu Search, Knapsack Problems}, doi = {doi:10.1109/CEC55065.2022.9870266}, notes = {Also known as \cite{9870266}}, ) @INPROCEEDINGS(Lorenci:2022:CEC, %xplor 24 Sep 2022 author = {Felipe Furtado Lorenci and Santiago Valdes Ravelo}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A New Integer Linear Program and A Grouping Genetic Algorithm with Controlled Gene Transmission for Joint Order Batching and Picking Routing Problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Efficiently managing large deposits and warehouses is not an easy task. The amount of variables and processes involved from the moment a consumer purchases a single product until its receipt is quite considerable. There are two major problems involving warehouses processes: the order picking problem (OPP) and the order batching problem (OBP). The OPP aims to minimize the distance traveled by a picker while collecting a set of products (orders). The OBP seeks to assign orders to batches with a capacity limit in order to minimize the sum of distances traveled during the retrieving of products from all batches. When these two problems are approached together, they become the Joint Order Batching and Picking Routing Problem (JOBPRP). This work proposes a novel formulation for JOBPRP and develops a grouping genetic algorithm with controlled gene transmission. To assess our proposals, we executed computational experiments over literature datasets. The mathematical model was used within a mixed-integer programming solver (Gurobi) and tested on the smaller instances to evaluate the quality of the solutions of our metaheuristic approach. Our computational results evidence high stability for all tested instances and much lower objective value than the previously reported in the literature, while maintaining a reasonable computational time.}, keywords = {Metaheuristics, Sociology, Programming, Routing, Stability analysis, Mathematical models, Proposals, Metaheuristic, Genetic Algorithms, Order Batching Problem, Order Picking Problem, Warehouses}, doi = {doi:10.1109/CEC55065.2022.9870210}, notes = {Also known as \cite{9870210}}, ) @INPROCEEDINGS(Elorza:2022:CEC, %xplor 24 Sep 2022 author = {Anne Elorza and Leticia Hernando and Jose A. Lozano}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Transitions from P to {NP-hardness:} the case of the Linear Ordering Problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={We decompose the linear ordering problem into a P and an NP-hard component by means of the Fourier transform. That is, we prove that the objective function can be expressed as the sum of two objective functions, one of which is associated with a P problem (an exact polynomial time algorithm is proposed to solve it), while the other is associated with an NP-hard problem. Based on this decomposition, we evaluate how different constructive algorithms whose behaviour only depends on univariate information degrade when the problem transits from P to NP-hard. A number of experiments are conducted with reduced dimensions, where the global optimum of the problems is known, giving different weights to the NP-hard component, while the weight of the P component is fixed.}, keywords = {Fourier transforms, NP-hard problem, Evolutionary computation, Linear programming, combinatorial optimization, permutations, linear ordering problem, NP-hardness, complexity transitions}, doi = {doi:10.1109/CEC55065.2022.9870392}, notes = {Also known as \cite{9870392}}, ) @INPROCEEDINGS(Nikolikj:2022:CEC, %xplor 24 Sep 2022 author = {Ana Nikolikj and Risto Trajanov and Gjorgjina Cenikj and Peter Korosec and Tome Eftimov}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Identifying minimal set of Exploratory Landscape Analysis features for reliable algorithm performance prediction}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Exploratory Landscape Analysis (ELA) enables the characterization of black-box optimization problem instances in the form of numerical features. Such features can be used to train a Machine Learning (ML) model to automatically predict the performance of an optimization algorithm on a specific problem instance. However, computing ELA features is a time consuming process and relatively expensive. In this paper, we aim to evaluate the usefulness of ELA features and identify features which are the most informative in automated algorithm performance prediction. The goal is to find a subset of features which are sufficient to train a reliable ML model for algorithm performance prediction, with reduced computational costs for calculating the ELA features. We focus on the performance prediction of the Covariance Matrix Adaptation Evolution Strat-egy (CMA-ES) algorithm on the COCO benchmark problems. The results showed that the number of ELA features that lead to a reliable algorithm performance prediction depends on the modular CMA-ES configuration under consideration. However, the set of features that are selected to be useful across different modular CMA-ES configurations are similar.}, keywords = {Training, Machine learning algorithms, Computational modeling, Pipelines, Predictive models, Benchmark testing, Prediction algorithms, automated algorithm performance prediction, exploratory landscape analysis, explainable models, feature selection}, doi = {doi:10.1109/CEC55065.2022.9870439}, notes = {Also known as \cite{9870439}}, ) @INPROCEEDINGS(Kolenovsky:2022:CEC, %xplor 24 Sep 2022 author = {Patrik Kolenovsky and Petr Bujok}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An adaptive variant of j{SO} with multiple crossover strategies employing Eigen transformation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this paper, new strategy options are developed for the adaptive jSO algorithm. The proposed variant of jSO is based on the competition of a binomial and exponential crossover. Moreover, an Eigen transformation approach is employed in the selected crossover with a given probability. The proposed variant of jSO is applied to the CEC 2022 benchmark set, which contains 12 functions with dimensionality $D=10$, 20. The proposed algorithm found the optima values in seven problems out of 24. When comparing the new variant of jSO with the original jSO algorithm, nine functions were improved, where two of them significantly.}, keywords = {Evolutionary computation, Benchmark testing, Convergence, jSO, multiple crossover strategies, Eigen transformation, experiments, test problems}, doi = {doi:10.1109/CEC55065.2022.9870378}, notes = {Also known as \cite{9870378}}, ) @INPROCEEDINGS(Acampora:2022:CEC, %xplor 24 Sep 2022 author = {Giovanni Acampora and Angela Chiatto and Autilia Vitiello}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Training Variational Quantum Circuits through Genetic Algorithms}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Recently, Variational Quantum Circuits (VQCs) are attracting considerable attention among quantum algorithms thanks to their robustness to the noise characterizing the current quantum devices. In detail, VQCs involve parameterized quan-tum circuits to be trained by means of a classical optimizer that makes queries to the quantum device. VQCs play a key role in several applications including quantum classifiers where the Hilbert space is used as feature space. Currently, the most used classical optimizer to learn V QCs is the gradient descent method. However, the so-called barren plateaus issue causes gradients of cost functions to become exceedingly small as the dimension of the classification problem is increased. As consequence, gradient descent method could be not efficient in real-world classification problems. This paper proposes to apply Genetic Algorithms (GAs) to train VQCs used as quantum classifiers. As shown in the experiments, the application of GAs results in accurate solutions obtained with a reduced number of queries to quantum devices.}, keywords = {Training, Systematics, Evolutionary computation, Cost function, Classification algorithms, Quantum circuit, Task analysis, Variational quantum circuits, Quantum Ma-chine Learning, Genetic Algorithms}, doi = {doi:10.1109/CEC55065.2022.9870242}, notes = {Also known as \cite{9870242}}, ) @INPROCEEDINGS(Dube:2022:CEC, %xplor 24 Sep 2022 author = {Michael Dube and Sheridan Houghten}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Now I Know My Alpha, Beta, Gammas: Variants in an Epidemic Scheme}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Personal contact networks are used to represent the social connections that exist between individuals within a population. Producing accurate networks that represent the actual vectors of infection that exist within a network can be useful for modelling epidemic trajectory and outcomes, which is significantly impacted by a network's structure. An evolutionary algorithm is used to evolve these networks subject to two fitness measures: epidemic duration and epidemic spread through a population. With each infection there is a small probability of a new variant being generated. Being infected with one variant provides partial immunity to future variants. This allows us to evaluate the impact of each variant, a significant innovation in comparison to other work. The amount by which each variant was allowed to change had a significant impact upon epidemic spread. For epidemic duration, the probability of new variants was the primary cause of increased epidemic duration.}, keywords = {Epidemics, Technological innovation, Sociology, Evolutionary computation, Trajectory, Statistics}, doi = {doi:10.1109/CEC55065.2022.9870391}, notes = {Also known as \cite{9870391}}, ) @INPROCEEDINGS(Levonyan:2022:CEC, %xplor 24 Sep 2022 author = {Karine Levonyan and Jesse Harder and Fernando {De Mesentier Silva}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Many games are reliant on creating new and engaging content constantly to maintain the interest of their player-base. One such example are puzzle games, in such it is common to have a recurrent need to create new puzzles. Creating new puzzles requires guaranteeing that they are solvable and interesting to players, both of which require significant time from the designers. Automatic validation of puzzles provides designers with a significant time saving and potential boost in quality. Automation allows puzzle designers to estimate different properties, increase the variety of constraints, and even personalize puzzles to specific players. Puzzles often have a large design space, which renders exhaustive search approaches infeasible, if they require significant time. Specifically, those puzzles can be formulated as quadratic combinatorial optimization problems. This paper presents an evolutionary algorithm, empowered by expert-knowledge informed heuristics, for solving logical puzzles in video games efficiently, leading to a more efficient design process. We discuss multiple variations of hybrid genetic approaches for constraint satisfaction problems that allow us to find a diverse set of near-optimal solutions for puzzles. We demonstrate our approach on a fantasy Party Building Puzzle game, and discuss how it can be applied more broadly to other puzzles to guide designers in their creative process.}, keywords = {Automation, Buildings, Games, Evolutionary computation, Genetics, Optimization, Genetic algorithms}, doi = {doi:10.1109/CEC55065.2022.9870402}, notes = {Also known as \cite{9870402}}, ) @INPROCEEDINGS(Acampora:2022:CEC, %xplor 24 Sep 2022 author = {Giovanni Acampora and Roberto Schiattarella and Autilia Vitiello}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Quantum Mating Operator: A New Approach to Evolve Chromosomes in Genetic Algorithms}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Genetic Algorithms (GAs) are optimization methods that search near-optimal solutions by applying well-known operations such as selection, crossover and mutation. In particular, crossover and mutation are aimed at creating new solutions from selected parents with the goal of discovering better and better solutions in the search space. In literature, several approaches have been defined to create new solutions from the mating pool to try to improve the performance of genetic optimization. In this paper, the literature is enriched by introducing a new mating operator that harnesses the stochastic nature of quantum computation to evolve individuals in a classical genetic workflow. This new approach, named Quantum Mating Operator, acts as a multi-parent operator that identifies alleles' frequency patterns from a collection of individuals selected by means of conventional selection operators, and encodes them through a quantum state. This state is successively mutated and measured to generate a new classical chromosome. As shown by experimental results, GAs equipped with the proposed operator outperform those equipped with traditional crossover and mutation operators when used to solve well-known benchmark functions.}, keywords = {Quantum computing, Quantum algorithm, Radiative recombination, Logic gates, Quantum state, Benchmark testing, Genetics, Quantum Computing, Genetic Algorithms, Mating Operators}, doi = {doi:10.1109/CEC55065.2022.9870425}, notes = {Also known as \cite{9870425}}, ) @INPROCEEDINGS(Buzdalov:2022:CEC, %xplor 24 Sep 2022 author = {Maxim Buzdalov}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={The $(1+(\lambda,\lambda))$ Genetic Algorithm on the Vertex Cover Problem: Crossover Helps Leaving Plateaus}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Many discrete optimization problems feature plateaus, which are hard to evolutionary algorithms due to the lack of fitness guidance. While higher mutation rates may assist in making a jump from the plateau to some better search point, an algorithm typically performs random walks on a plateau, possibly with some assistance from diversity mechanisms. The vertex cover problem is one of the important NP-hard problems. We found that the recently proposed $(1+(\lambda, \lambda))$ genetic algorithm solves certain instances of this problem, including those that are hard to heuristic solvers, much faster than simpler mutation-only evolutionary algorithms. Our theoretical analysis shows that there exists an intricate interplay between the problem structure and the way crossovers are used. It results in a drift towards the points where finding the next improvement is much easier. While this condition is formally proven only on one class of instances and for a subset of search points, experiments show that it is responsible for performance improvements in a much larger range of cases.}, keywords = {NP-hard problem, Evolutionary computation, Search problems, Optimization, Genetic algorithms}, doi = {doi:10.1109/CEC55065.2022.9870224}, notes = {Also known as \cite{9870224}}, ) @INPROCEEDINGS(Santos:2022:CEC, %xplor 24 Sep 2022 author = {Raphael Gomes Santos and Alexandre Plastino and Alexandre C. M. {de Oliveira}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={{DMC-GRASP:} A Continuous {GRASP} hybridized with Data Mining}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The hybridization of metaheuristics with data mining techniques has been successfully applied to combinatorial optimization problems. Examples of this type of strategy are DM-GRASP and MDM-GRASP, hybrid versions of the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic, which incorporate data mining techniques. This type of hybrid method is called Data-Driven Metaheuristics and aims at extracting useful knowledge from the data generated by metaheuristics in their search process. Despite success in combinatorial problems like the set packing problem and maximum diversity problem, proposals of this type to solve continuous optimization problems are still scarce in the literature. This work presents a data mining hybrid version of C-GRASP, an adaptation of GRASP for problems with continuous variables. We call this new version DMC-GRASP, which identifies patterns in high-quality solutions and generates new solutions guided by these patterns. We performed computational experiments with DMC-GRASP on a set of well-known mathematical benchmark functions, and the results showed that metaheuristics for continuous optimization could also benefit from using patterns to guide the search for better solutions.}, keywords = {Metaheuristics, Evolutionary computation, Benchmark testing, Hybrid power systems, Data mining, Proposals, Convergence, Metaheuristics, Data Mining, Continuous Opti-mization}, doi = {doi:10.1109/CEC55065.2022.9870264}, notes = {Also known as \cite{9870264}}, ) @INPROCEEDINGS(Kaluthantrige:2022:CEC, %xplor 24 Sep 2022 author = {Aurelio Kaluthantrige and Jinglang Feng and Jesus Gil-Fernandez and Andrea Pellacani}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Centroid regression using {CNN-based} Image Processing Algorithm with application to a binary asteroid system}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Autonomous Optical Navigation is essential for the proximity operations of space missions to asteroids that usually have irregular gravity fields. One core component of this navigation strategy is the Image Processing (IP) algorithm that extracts optical observables from images captured by the spacecraft's on-board camera. Among these observables, the centroid of the asteroid is important to determine the position between the spacecraft and the body, which is the focus of this research. However, the performance of standard IP algorithms is affected and constrained by the features of the images, such as the shape of the asteroid, the illumination conditions and the presence of additional bodies, therefore, the quality of the extracted optical observables is influenced. To address the latter two challenges, this paper develops a Convolutional Neural Networks (CNN)-based IP algorithm and applies it to the Early Characterization Phase (ECP) of the European Space Agency's HERA mission with the target body of binary asteroid Didymos. This algorithm is capable of estimating the centroid of the primary body successfully with high accuracy and without being affected by the presence of the secondary body or the illumination in the input images. In addition, it can also estimate the centroid of the secondary body when the two bodies are in the same image, which increases the robustness of the overall navigation strategy.}, keywords = {Space vehicles, Navigation, Software algorithms, Asteroids, Lighting, Optical imaging, Feature extraction, Centroiding technique, image processing, phase angle, Convolutional Neural Networks, asteroid exploration}, doi = {doi:10.1109/CEC55065.2022.9870217}, notes = {Also known as \cite{9870217}}, ) @INPROCEEDINGS(Peerlinck:2022:CEC, %xplor 24 Sep 2022 author = {Amy Peerlinck and John Sheppard}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-Objective Factored Evolutionary Optimization and the Multi-Objective Knapsack Problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={We propose a factored evolutionary framework for multi-objective optimization that can incorporate any multi-objective population based algorithm. Our framework, which is based on Factored Evolutionary Algorithms, uses overlapping subpopulations to increase exploration of the objective space; however, it also allows for the creation of distinct subpopulations as in co-operative co-evolutionary algorithms (CCEA). We apply the framework with the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), resulting in Factored NSGA-II. We compare NSGA-II, CC-NSGA-II, and F-NSGA-II on two different versions of the multi-objective knapsack problem. The first is the classic binary multi-knapsack implementation introduced by Zitzler and Thiele, where the number of objectives equals the number of knapsacks. The second uses a single knapsack where, aside from maximizing profit and minimizing weight, an additional objective tries to minimize the difference in weight of the items in the knapsack, creating a balanced knapsack. We further extend this version to minimize volume and balance the volume. The proposed 3-to-5 objective balanced single knapsack problem poses a difficult problem for multi-objective algorithms. Our results indicate that the non-dominated solutions found by F-NSGA-II tend to cover more of the Pareto front and have a larger hypervolume.}, keywords = {Measurement, Sociology, Evolutionary computation, Statistics, Optimization, Sorting, Genetic algorithms, multi-objective combinatorial optimization, co-operative coevolution, non-dominated sorting genetic algorithm, multi-objective knapsack}, doi = {doi:10.1109/CEC55065.2022.9870377}, notes = {Also known as \cite{9870377}}, ) @INPROCEEDINGS(MacLachlan:2022:CEC, %xplor 24 Sep 2022 author = {Jordan MacLachlan and Yi Mei and Fangfang Zhang and Mengjie Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Genetic Programming for Vehicle Subset Selection in Ambulance Dispatching}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Assigning ambulances to emergencies in real-time, ensuring both that patients receive adequate care and that the fleet remains capable of responding to any potential new emergency, is a critical component of any ambulance service. Thus far, most techniques to manage this problem are as convoluted as the problem itself. As such, many real-world medical services resort to using the naive closest-idle rule, whereby the nearest available vehicles are dispatched to serve each new call. This paper explores the feasibility of using a genetic programming hyper heuristic (GPHH) in order to generate intelligible rules of thumb to select which vehicles should attend any given emergency. Such rules, either manually or automatically designed, are evaluated within a novel solution construction procedure which constructs solutions to the ambulance dispatching problem given the parameters of the simulation environment. Experimental results suggest that GPHH is a promising technique to use when approaching the ambulance dispatching problem. Further, a GPHH-evolved rule's interpretability allows for detailed semantic analysis into which features of the environment are valuable to the decision making process, allowing for human dispatching agents to make more informed decisions in practice.}, keywords = {Decision making, Semantics, Genetic programming, Medical services, Evolutionary computation, Dispatching, Real-time systems, Hyper Heuristic, Genetic Programming, Ambulance Dispatch, Evolutionary Computation}, doi = {doi:10.1109/CEC55065.2022.9870323}, notes = {Also known as \cite{9870323}}, ) @INPROCEEDINGS(Jocko:2022:CEC, %xplor 24 Sep 2022 author = {Pawel Jocko and Beatrice M. Ombuki-Berman and Andries P. Engelbrecht}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Dynamic Multi-objective Optimisation Using Multi-guide Particle Swarm Optimisation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This study conducts a sensitivity analysis of the recently proposed multi-guide particle swarm optimisation (MG-PSO) algorithm for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-swarm approach where each subswarm optimises one of the objectives. This paper further adapts the MGPSO algorithm to solve DMOPs by proposing alternative balance coefficient update strategies to allow efficient tracking of the changing Pareto-optimal front (POF). A total of twenty-nine benchmark functions and six performance measures were implemented to help with this task. The experiments were run against five different environment types to determine whether the MGPSO can solve problems with various spatial and temporal severities. The best control parameter update strategy was then compared with other state-of-the-art dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that MGPSO with the balance coefficient parameter re-initialized after the environment change achieves very competitive and oftentimes better performance when compared with the competing algorithms.}, keywords = {Optical fibers, Quantum computing, Sensitivity analysis, Heuristic algorithms, Scalability, Benchmark testing, Linear programming, Dynamic Multi-objective Optimisation, Multi-guide Particle Swarm Optimisation}, doi = {doi:10.1109/CEC55065.2022.9870299}, notes = {Also known as \cite{9870299}}, ) @INPROCEEDINGS(Sallam:2022:CEC, %xplor 24 Sep 2022 author = {Karam M. Sallam and Mohamed Abdel-Basset and Mohammed El-Abd and Ali Wagdy}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={{IMODEII:} an Improved {IMODE} algorithm based on the Reinforcement Learning}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The success of differential evolution algorithm depends on its offspring breeding strategy and the associated control parameters. Improved Multi-Operator Differential Evolution (IMODE) proved its efficiency and ranked first in the CEC2020 competition. In this paper, an improved IMODE, called IMODEII, is introduced. In IMODEII, Reinforcement Learning (RL), a computational methodology that simulates interaction-based learning, is used as an adaptive operator selection approach. RL is used to select the best-performing action among three of them in the optimization process to evolve a set of solution based on the population state and reward value. Different from IMODE, only two mutation strategies have been used in IMODEII. We tested the performance of the proposed IMODEII by considering 12 benchmark functions with 10 and 20 variables taken from CEC2022 competition on single objective bound constrained numerical optimisation. A comparison between the proposed IMODEII and the state-of-the-art algorithms is conducted, with the results demonstrating the efficiency of the proposed IMODEII.}, keywords = {Sociology, Reinforcement learning, Evolutionary computation, Benchmark testing, Solar system, Statistics, Optimization, reinforcement learning, differential evolution, evolutionary algorithms, unconstrained optimisation}, doi = {doi:10.1109/CEC55065.2022.9870420}, notes = {Also known as \cite{9870420}}, ) @INPROCEEDINGS(Junior:2022:CEC, %xplor 24 Sep 2022 author = {Joao Roberto Bertini Junior and Alberto Cano}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An Explainable Classifier based on Genetically Evolved Graph Structures}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Trusting an algorithmic decision is much easier if it is understood how it was achieved. Therefore, data mining algorithms with explainable abilities are preferred over complex models for critical applications. Rule-based algorithms are among the easiest data classification models to understand. However, as most interpretable models, rule-based algorithms do not provide the highest accuracy. The Attribute-based Decision Graph (AbDG) is a model to represent a labeled data set as a weighted graph over the attributes. When associated with a proper algorithm, AbDGs can be used for supervised data mining tasks. An important aspect of AbDGs is that the graph encompasses the original attribute values and their relationships, which makes it easily interpretable by extracting rules. The AbDG drawback is defining a suitable graph structure for a given data set. In previous works, the authors proposed GA-AbDG, a genetic algorithm to search for an AbDG by evolving its edge set, keeping the vertex set fixed. In this paper, we propose an evolutionary algorithm and genetic operators to evolve both, vertex and edge sets, enhancing the search space of possible AbDG structures. Moreover, we associate a rule-based classifier with the AbDG to achieve explainable results. Experimental results show the proposed method outperforms GA-AbDG and five other classical interpretable classification algorithms.}, keywords = {Knowledge based systems, Evolutionary computation, Genetics, Data models, Classification algorithms, Topology, Data mining, Attribute-based Decision Graphs, Explainable Classification, Genetically Evolving Classifiers}, doi = {doi:10.1109/CEC55065.2022.9870293}, notes = {Also known as \cite{9870293}}, ) @INPROCEEDINGS(Matousek:2022:CEC, %xplor 24 Sep 2022 author = {Radomil Matousek and Tomas Hulka}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Stabilization of Higher Periodic Orbits of the Duffing Map using Meta-evolutionary Approaches: A Preliminary Study}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper deals with an advanced adjustment of stabilization sequences for complex chaotic systems by means of meta-evolutionary approaches in the form of a preliminary study. In this study, a two-dimensional discrete-time dynamic system denoted as Duffing map, also called Holmes map, was used. In general, the Duffing oscillator model represents a real system in the field of nonlinear dynamics. For example, an excited model of a string choosing between two magnets. There are many articles on the stabilization of various chaotic maps, but attempts to stabilize the Duffing map, moreover, for higher orbits, are rather the exception. In the case of period four, this is a novelty. This paper presents several approaches to obtaining stabilizing perturbation sequences. The problem of stabilizing the Duffing map turns out to be difficult and is a good challenge for metaheuristic algorithms, and also as benchmark function. The first approach is the optimal parameterization of the ETDAS model using multi-restart Nelder-Mead (NM) algorithm na Genetic Algorithm (GA). The second approach is to use the symbolic regression procedure. A perturbation model is obtained using Genetic Programming (GP). The third approach is two-level optimization, where the best GP model is subsequently optimized using NM and GA algorithms. A novelty of the approach is also the effective use of the objective function, precisely in relation to the process of optimization of higher periodic paths.}, keywords = {Chaos, Perturbation methods, Simulation, Metaheuristics, Time series analysis, Genetic programming, Linear programming, Chaos control, Evolutionary computation, Genetic Programming, Genetic Algorithm, Nelder-Mead Algorithm, Duffing map, Optimization}, doi = {doi:10.1109/CEC55065.2022.9870372}, notes = {Also known as \cite{9870372}}, ) @INPROCEEDINGS(Toscano:2022:CEC, %xplor 24 Sep 2022 author = {Gregorio Toscano and J. Sebastian Hernandez-Suarez and Julian Blank and Pouyan Nejadhashemi and Kalyanmoy Deb and Lewis Linker}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Large-scale Multi-objective Optimization for Water Quality in Chesapeake Bay Watershed}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The careful selection of Best Management Practices (BMPs) to reduce loading, such as nitrogen, phosphorus, and sediments, can substantially improve the water quality of water-sheds. This paper introduces the first implementation of a hybrid and customized evolutionary multi-objective (EMO) algorithm to improve the Chesapeake Bay Watershed's (CBW) water quality. To make the algorithm scalable, we inject a few solutions obtained using an integer programming algorithm (IPOPT) in the initial population of EMO. Also, a repair operator is applied to satisfy every equality constraint. Combining these approaches can find a set of non-dominated trade-off solutions from 1,012 variables (Tucker county in West Virginia) to a staggering 153,818 variable problem (the whole state of West Virginia). Furthermore, a pre-liminary analysis of obtained trade-off solutions finds interesting properties of BMP allocations, providing an optimistic picture of applying the proposed customized optimization algorithm in addressing other bigger states leading to the whole Chesapeake Bay watershed.}, keywords = {Sociology, Water quality, Evolutionary computation, Programming, Phosphorus, Sediments, Resource management, Watershed Optimization, Large-scale Optimization, Multi-objective Optimization, Hybrid approach}, doi = {doi:10.1109/CEC55065.2022.9870286}, notes = {Also known as \cite{9870286}}, ) @INPROCEEDINGS(Green:2022:CEC, %xplor 24 Sep 2022 author = {Michael Cerny Green and Ahmed Khalifa and M Charity and Debosmita Bhaumik and Julian Togelius}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Predicting Personas Using Mechanic Frequencies and Game State Traces}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={We investigate how to efficiently predict play personas based on playtraces. Play personas can be computed by calculating the action agreement ratio between a player and a generative model of playing behavior, a so-called procedural persona. But this is computationally expensive and assumes that appropriate procedural personas are readily available. We present two methods for estimating play personas, one using regular supervised learning and aggregate measures of game mechanics initiated, and another based on sequence learning on a trace of closely cropped gameplay observations. While both of these methods achieve high accuracy when predicting play personas defined by agreement with procedural personas, they utterly fail to predict play style as defined by the players themselves using a questionnaire. This interesting result highlights the value of using computational methods in defining play personas.}, keywords = {Computational modeling, Aggregates, Supervised learning, Games, Evolutionary computation, Predictive models, Mechanical variables measurement, game mechanics, machine learning, play persona, player modeling, videogames}, doi = {doi:10.1109/CEC55065.2022.9870406}, notes = {Also known as \cite{9870406}}, ) @INPROCEEDINGS(Russo:2022:CEC, %xplor 24 Sep 2022 author = {Igor L.S. Russo and Helio J.C. Barbosa}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A multitasking surrogate-assisted differential evolution method for solving bi-level optimization problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Bi-level programming (BLP) is a hierarchical decision-making problem in which part of the constraints is determined by solving other optimization problems. Classic op-timization techniques cannot be applied directly, while standard metaheuristics often demand high computational costs. The transfer optimization paradigm uses the experience acquired when solving one optimization problem to speed up a distinct but related task. In particular, the multitasking technique ad-dresses two or more optimization tasks simultaneously to explore similarities and improve convergence. BLPs can benefit from multitasking as many (potentially similar) lower-level problems must be solved. Recently, several studies used surrogate methods to save expensive upper-level function evaluations in BLPs. This work proposes an algorithm based on Differential Evolution supported by transfer optimization and surrogate models to solve BLPs more efficiently. Experiments show a reduction of up to 8percent regarding the number of function evaluations of the upper-level problem while achieving similar or superior accuracy when compared to state-of-the-art solvers.}, keywords = {Metaheuristics, Decision making, Evolutionary computation, Programming, Multitasking, Computational efficiency, Task analysis}, doi = {doi:10.1109/CEC55065.2022.9870241}, notes = {Also known as \cite{9870241}}, ) @INPROCEEDINGS(Tanaka:2022:CEC, %xplor 24 Sep 2022 author = {Shoichiro Tanaka and Keiki Takadama and Hiroyuki Sato}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Impacts of Single-objective Landscapes on Multi-objective Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This work revealed a relationship between a multi-objective optimization problem and single-objective optimization problems that exist in the multi-objective problem. This work focused on combinatorial problems and investigated the relations between the local optima networks of the single-objective problems and the Pareto optima network of the multi-objective problem. Each of their networks has a graph structure. We divided the entire network into subgraphs. Each subgraph was called a component and characterized by overlapping relations between the single-objective local optima networks and the multi-objective Pareto optima network. Results on multi-objective landscape problems showed that most Pareto optimal solutions were reachable from the single-objective local optimal solutions. This tendency was emphasized by increasing the number of objectives and the objective correlation. The number of co-variables impacted the number of cross-link relations between the single-objective local optima networks and the multi-objective Pareto optima network. The results suggested that searching for single-objective problems is a clue to multi-objective optimization.}, keywords = {Correlation, Evolutionary computation, Pareto optimization, Search problems, Optimization, local optima network, Pareto graph, multi-objective optimization, combinatorial optimization}, doi = {doi:10.1109/CEC55065.2022.9870226}, notes = {Also known as \cite{9870226}}, ) @INPROCEEDINGS(Ozaki:2022:CEC, %xplor 24 Sep 2022 author = {Yoshihiko Ozaki and Shintaro Takenaga and Masaki Onishi}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Global Search versus Local Search in Hyperparameter Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Hyperparameter optimization (HPO) is a compu-tationally expensive blackbox optimization problem to maximize the performance of a machine learning model by tuning the model hyperparameters. Conventionally, global search has been widely adopted rather than local search to address HPO. In this study, we investigate whether this conventional choice is reasonable by empirically comparing popular global and local search methods as applied to HPO problems. The numerical results demonstrate that local search methods consistently achieve results that are comparable to or better than those of the global search methods, i.e., local search is a more reasonable choice for HPO. We also report the findings of detailed analyses of the experimental data conducted to understand how each method functions and the objective landscapes of HPO.}, keywords = {Search methods, Machine learning, Evolutionary computation, Numerical models, Optimization, Tuning, Hyperparameter Optimization, Automated Ma-chine Learning, Deep Learning, Global Search, Local Search}, doi = {doi:10.1109/CEC55065.2022.9870287}, notes = {Also known as \cite{9870287}}, ) @INPROCEEDINGS(Fritzsch:2022:CEC, %xplor 24 Sep 2022 author = {Clemens Fritzsch and Jorn Hoffmann and Martin Bogdan}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving Hardware by Direct Bitstream Manipulation of a Modern FPGA}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Field-programmable gate arrays (FPGAs) have re-cently received renewed attention in the context of Evolvable Hardware (EHW). The most fine grained approach to changing their internal structure, direct manipulation of the bitstream, has largely been abandoned. The undocumented bitstream formats of modern FPGAs made it complicated and error-prone. This situation has fundamentally changed with the advent of open-source FPGA toolchains. Previous attempts to exploit this opportunity were promising, but only manged to solve very basic tasks. We present in this paper an evolved tone discriminator circuit. It was evolved by replicating the most famous experiment in this field, but with modern hardware. For that we map the originally used Xilinx XC6200 FPGA to a modern Lattice iCE40 FPGA. We show how to set up the experiment and optimize the evolution environment. Our approach allows over 130 times more reconfigurations per second than previous approaches. Additionally, we discuss reasons for the abandonment of direct bitstream manipulation for EHW in context of the new possibilities created by open-source FPGA toolchains. We show which challenges have been solved and which steps need to be taken next.}, keywords = {Lattices, Computer architecture, Evolutionary computation, Hardware, Security, Task analysis, Field programmable gate arrays}, doi = {doi:10.1109/CEC55065.2022.9870297}, notes = {Also known as \cite{9870297}}, ) @INPROCEEDINGS(Rogers:2022:CEC, %xplor 24 Sep 2022 author = {Brendan Rogers and Nasimul Noman and Stephan Chalup and Pablo Moscato}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Joint Optimization of Topology and Hyperparameters of Hybrid {DNNs} for Sentence Classification}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Deep Neural Networks (DNN) require specifically tuned architectures and hyperparameters when being applied to any given task. Nature-inspired algorithms have been successfully applied for optimising various hyperparameters in different types of DNNs such as convolutional and recurrent for sentence classification. Hybrid networks, which contain multiple types of neural architectures have more recently been used for sentence classification in order to achieve better performance. However, the inclusion of hybrid architectures creates numerous possibilities of designing the network and those sub-networks also need fine-tuning. At present these hybrid networks are designed manually and various organisation attempts are noticed. In order to understand the benefit and the best design principle of such hybrid DNNs for sentence classification, in this work we used an Evolutionary Algorithm (EA) to optimise the topology and various hyperparameters in different types of layers within the network. In our experiments, the proposed EA designed the hybrid networks by using a single dataset and evaluated the evolved networks on multiple other datasets to validate their generalisation capability. We compared the EA-designed hybrid networks with human-designed hybrid networks in addition to other EA-optimised and expert-designed non-hybrid architectures.}, keywords = {Deep learning, Network topology, Neural networks, Computer architecture, Evolutionary computation, Topology, Classification algorithms}, doi = {doi:10.1109/CEC55065.2022.9870285}, notes = {Also known as \cite{9870285}}, ) @INPROCEEDINGS(Yatsu:2022:CEC, %xplor 24 Sep 2022 author = {Naoya Yatsu and Hiroki Shiraishi and Hiroyuki Sato and Keiki Takadama}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={{XCSR} with {VAE} using Gaussian Distribution Matching: From Point to Area Matching in Latent Space for Less-overlapped Rule Generation in Observation Space}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper focuses on the matching mechanism of Learning Classifier System (LCS) in a continuous space and proposes a novel matching mechanism based on Gaussian distribution. This mechanism can match the "area" instead of the "point (one value)" in the continuous space unlike the conventional LCS such as XCSR (XCS with Continuous-Valued Inputs). Such an area matching contributes to generating the rules (called classifiers) with less-overlapped with other rules. Concretely, the proposed area matching mechanism employed in XCSR using VAE can generate appropriate classifiers for latent variables with high-dimensional inputs by VAE and create a human-interpretable observation space of human-interpretable classifiers. Since the latent variable in VAE is followed by Gaus-sian distribution, the following three matching mechanisms are compared: (i) the (single) point matching that selects the classifier which condition covers the mean of Gaussian distribution M; (ii) the multiple points matching that selects the classifier which condition covers the data sampled from Gaussian distribution (M, u); and (iii) the area matching that selects the classifier which condition roughly covers a certain area of Gaussian distribution (M, o). Through the intensive experiments on the high dimension maze problem, the following implications have been revealed: (1) the point matching in XCSR with VAE generates the ambiguous classifiers which conditions are overlapped with the other classifiers with the different action; (2) the sampling multiple points matching in XCSR with VAE has a potential of generating the less-overlapped classifiers by improving the data set through sampling. (3) the proposed area matching can generate the less-overlapped classifiers with the same learning steps, which corresponds to the time of the point matching.}, keywords = {Image color analysis, Evolutionary computation, Gaussian distribution, variational autoencoder, data mining}, doi = {doi:10.1109/CEC55065.2022.9870349}, notes = {Also known as \cite{9870349}}, ) @INPROCEEDINGS(Salman:2022:CEC, %xplor 24 Sep 2022 author = {Ozgur Salman and Michael Kampouridis and Delaram Jarchi}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Trading Strategies Optimization by Genetic Algorithm under the Directional Changes Paradigm}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The subject of financial forecasting has been re-searched for decades, and the driver behind its measured data has been fuelled by the selection of physical time series, which summarize data using fixed time intervals. For instance, time-series for daily stock data would be profiled at 252 points in one year. However, this episodic style neglects the important events, or price changes that occur between two intervals. Thus, we use Directional Changes (DC) as an event-based series, which is an alternative way to record price movements. In DC, unlike time-series methods, time intervals are constituted by price changes. The unique feature that decides the price change to be considered as a significant is called a threshold ?. The objective of our paper is to create DC-based trading strategies, and then optimize them using a Genetic Algorithm (GA). To construct such strategies, we use DC-based indicators and scaling laws that have been empirically identified under DC summaries. We first propose four novel DC-based trading strategies and then combine them with existing DC-based strategies and finally optimize them via the GA. We conduct trading experiments over 44 stocks. Results show that the GA-optimized strategies are able to generate new and profitable trading strategies, significantly outperforming the individual DC-based strategies, as well as a buy and sell benchmark.}, keywords = {Time series analysis, Evolutionary computation, Benchmark testing, Time measurement, Forecasting, Optimization, Genetic algorithms, directional changes, trading strategies, genetic algorithm, financial forecasting, machine learning}, doi = {doi:10.1109/CEC55065.2022.9870270}, notes = {Also known as \cite{9870270}}, ) @INPROCEEDINGS(Parque:2022:CEC, %xplor 24 Sep 2022 author = {Victor Parque}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Learning Obstacle-Avoiding Lattice Paths using Swarm Heuristics: Exploring the Bijection to Ordered Trees}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Lattice paths are functional entities that model efficient navigation in discrete/grid maps. This paper presents a new scheme to generate collision-free lattice paths with utmost efficiency using the bijective property to rooted ordered trees, rendering a one-dimensional search problem. Our computational studies using ten state-of-the-art and relevant nature-inspired swarm heuristics in navigation scenarios with obstacles with convex and non-convex geometry show the practical feasibility and efficiency in rendering collision-free lattice paths. We believe our scheme may find use in devising fast algorithms for planning and combinatorial optimization in discrete maps.}, keywords = {Geometry, Operations research, Navigation, Law, Lattices, Rendering (computer graphics), Search problems, path planning, lattice paths, ordered trees, enumeration, combinatorial objects, Catalan numbers}, doi = {doi:10.1109/CEC55065.2022.9870344}, notes = {Also known as \cite{9870344}}, ) @INPROCEEDINGS(Shiraishi:2022:CEC, %xplor 24 Sep 2022 author = {Hiroki Shiraishi and Yohei Havamizu and Hiroyuki Sato and Keiki Takadama}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Beta Distribution based {XCS} Classifier System}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper proposes the Beta Distribution based XCS Classifier System (called j3-XCS) as the novel XCS having the new representation (1) that can handle curved surface hyperpolyhedral conditions, including hyperellipsoids, (2) that can "quickly" and "stably" evolve classifiers that appropri-ately classify the area, and (3) that is robust to the initial hyperparameters of the representation. Concretely, j3-XCS is composed of classifiers that condition part in each dimension is represented by the beta distribution that can change a flexible distribution shape according to its parameters. Through the intensive experiments of the different types of continuous space problems, the following implications have been revealed: (1) j3-X CS can show higher classification performance and function approximation performance with fewer classifiers than other XCSs with the conventional representations such as XCS with the hyperrectangular representation (i.e., XCSR) and XCS with the hyperellipsoidal representations (i.e., hyperellipsoid-based XCS); (2) fJ-XCS can quickly and stably evolve the classifiers that can appropriately match the line and curved shapes in comparison with XCSR and the ellipsoidal-based XCS; and (3) while the performance of the conventional XCSs is highly sensitive to the hyperparameter that defines the generality of the covering classifier, the performance of fJ-XCS is the most robust to its values.}, keywords = {Shape, Evolutionary computation, Function approximation, Learning classifier systems, XCS classifier system, representation, beta distribution}, doi = {doi:10.1109/CEC55065.2022.9870314}, notes = {Also known as \cite{9870314}}, ) @INPROCEEDINGS(Paardekooper:2022:CEC, %xplor 24 Sep 2022 author = {Cornelius Paardekooper and Nasimul Noman and Raymond Chiong and Vijay Varadharajan}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification. CNNs, which are originally designed for image processing, identify malware binaries visualised as images. Despite offering promising performance, these human-designed networks are very large requiring more resources to train and deploy them. Evolutionary algorithms have been successfully used in designing deep neural networks automatically for different application domains. In this work, we use a Genetic Algorithm (GA) to optimise the CNN topology and hyperparameters for image-based malware classification. Computational experiments with two different malware datasets, Malimg and Microsoft Malware, show that the GA-evolved networks are very competitive to the networks designed by experts in classifying malware, yet they are also considerably smaller in size comparison.}, keywords = {Visualization, Network topology, Neural networks, Evolutionary computation, Computer architecture, Malware, Robustness, Image-based Malware Classification, Convolutional Neural Network, Genetic Algorithm, Deep Neuroevolution}, doi = {doi:10.1109/CEC55065.2022.9870218}, notes = {Also known as \cite{9870218}}, ) @INPROCEEDINGS(Plump:2022:CEC, %xplor 24 Sep 2022 author = {Christina Plump and Bernhard J. Berger and Rolf Drechsler}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Using density of training data to improve evolutionary algorithms with approximative fitness functions}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Evolutionary algorithms are a well-known optimisation technique, especially for non-convex, multi-modal optimisation problems. Their capability of adjusting to different search spaces and tasks by choosing the suitable encoding and operators has led to their widespread use in various application domains. However, application domains sometimes come with difficulties like fitness functions that can not be evaluated or not more than a few times. In these situations, surrogate functions or approximative fitness functions allow the evolutionary algorithm to work despite this complication. Still, using approximative fitness functions comes with a price: The fitness value is no longer correct for every individual, and the algorithm can not know which value to trust. However, statistical methods yield knowledge about the preciseness of the approximation. We propose using this knowledge to adapt the fitness value to ease the effects of the approximative nature. We choose to use the information given in the density of the training data, which has computational merits over the use of other techniques like cross-validation or prediction intervals. We evaluate our method on four well-known benchmark functions and achieve good optimisation success and computation time results.}, keywords = {Statistical analysis, Training data, Evolutionary computation, Benchmark testing, Approximation algorithms, Encoding, Task analysis}, doi = {doi:10.1109/CEC55065.2022.9870352}, notes = {Also known as \cite{9870352}}, ) @INPROCEEDINGS(Jankovic:2022:CEC, %xplor 24 Sep 2022 author = {Anja Jankovic and Diederick Vermetten and Ana Kostovska and Jacob {de Nobel} and Tome Eftimov and Carola Doerr}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Trajectory-based Algorithm Selection with Warm-starting}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Landscape-aware algorithm selection approaches have so far mostly been relying on landscape feature extraction as a preprocessing step, independent of the execution of optimization algorithms in the portfolio. This introduces a significant overhead in computational cost for many practical applications, as features are extracted and computed via sampling and evaluating the problem instance at hand, similarly to what the optimization algorithm would perform anyway within its search trajectory. As suggested in [Jankovic et al., EvoAPP 2021], trajectory-based algorithm selection circumvents the problem of costly feature extraction by computing landscape features from points that a solver sampled and evaluated during the optimization process. Features computed in this manner are used to train algorithm performance regression models, upon which a per-run algorithm selector is then built. In this work, we apply the trajectory-based approach onto a portfolio of five algorithms. We study the quality and accuracy of performance regression and algorithm selection models in the scenario of predicting different algorithm performances after a fixed budget of function evaluations. We rely on landscape features of the problem instance computed using one portion of the aforementioned budget of the same function evaluations. Moreover, we consider the possibility of switching between the solvers once, which requires them to be warm-started, i.e. when we switch, the second solver continues the optimization process already being initialized appropriately by making use of the information collected by the first solver. In this new context, we show promising performance of the trajectory-based per-run algorithm selection with warm-starting.}, keywords = {Heuristic algorithms, Computational modeling, Switches, Predictive models, Prediction algorithms, Feature extraction, Search problems, dynamic algorithm selection, exploratory landscape analysis, evolutionary computation, black-box optimization}, doi = {doi:10.1109/CEC55065.2022.9870222}, notes = {Also known as \cite{9870222}}, ) @INPROCEEDINGS(Parque:2022:CEC, %xplor 24 Sep 2022 author = {Victor Parque}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Towards Hexapod Gait Adaptation using Enumerative Encoding of Gaits: Gradient-Free Heuristics}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The quest for the efficient adaptation of multilegged robotic systems to changing conditions is expected to render new insights into robotic control and locomotion. In this paper, we study the performance frontiers of the enumerative (factorial) encoding of hexapod gaits for fast recovery to conditions of leg failures. Our computational studies using five nature-inspired gradient-free optimization heuristics have shown that it is possible to render feasible recovery gait strategies that achieve minimal deviation to desired locomotion directives with a few evaluations (trials). For instance, it is possible to generate viable recovery gait strategies reaching 2.5 cm, (10 cm.) deviation on average with respect to a commanded direction with 40 - 60 (20) evaluations/trials. Our results are the potential to enable efficient adaptation to new conditions and to explore further the canonical representations for adaptation in robotic locomotion problems.}, keywords = {Legged locomotion, Fault tolerance, Adaptive systems, Fault tolerant systems, Parallel processing, Encoding, Particle swarm optimization, hexapod, gait adaptation, enumerative encoding, particle swarm optimization}, doi = {doi:10.1109/CEC55065.2022.9870257}, notes = {Also known as \cite{9870257}}, ) @INPROCEEDINGS(Saletta:2022:CEC, %xplor 24 Sep 2022 author = {Martina Saletta and Claudio Ferretti}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Grammar-based Evolutionary Approach for Assessing Deep Neural Source Code Classifiers}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Neural networks for source code processing have proven to be effective for solving multiple tasks, such as locating bugs or detecting vulnerabilities. In this paper, we propose an evolutionary approach for probing the behaviour of a deep neural source code classifier by generating instances that sample its input space. First, we apply a grammar-based genetic algorithm for evolving Python functions that minimise or maximise the probability of a function to be in a certain class, and we also produce programs that yield an output near to the classification threshold, namely for which the network does not express a clear classification preference. We then use such sets of evolved programs as initial popu-lations for an evolution strategy approach in which we apply, by following different policies, constrained small mutations to the individuals, so to both explore the decision boundary of the network and to identify the features that most contribute to a particular prediction. We furtherly point out how our approach can be effectively used for several tasks in the scope of the interpretable machine learning, such as for producing adversarial examples able to deceive a network, for identifying the most salient features, and further for characterising the abstract concepts learned by a neural model.}, keywords = {Codes, Computational modeling, Neural networks, Computer bugs, Machine learning, Evolutionary computation, Task analysis, structured grammatical evolution, evolution strategy, decision boundaries, deep neural networks, source code classifiers}, doi = {doi:10.1109/CEC55065.2022.9870317}, notes = {Also known as \cite{9870317}}, ) @INPROCEEDINGS(Martins:2022:CEC, %xplor 24 Sep 2022 author = {Miguel Martins and Miguel Rocha and Vitor Pereira}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Variational Autoencoders and Evolutionary Algorithms for Targeted Novel Enzyme Design}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Recent developments in Generative Deep Learning have fostered new engineering methods for protein design. Although deep generative models trained on protein sequence can learn biologically meaningful representations, the design of proteins with optimised properties remains a challenge. We combined deep learning architectures with evolutionary computation to steer the protein generative process towards specific sets of properties to address this problem. The latent space of a Variational Autoencoder is explored by evolutionary algorithms to find the best candidates. A set of single-objective and multi-objective problems were conceived to evaluate the algorithms' capacity to optimise proteins. The optimisation tasks consider the average proteins' hydrophobicity, their solubility and the probability of being generated by a defined functional Hidden Markov Model profile. The results show that Evolutionary Algorithms can achieve good results while allowing for more variability in the design of the experiment, thus resulting in a much greater set of possibly functional novel proteins.}, keywords = {Proteins, Deep learning, Computational modeling, Biological system modeling, Transfer learning, Hidden Markov models, Evolutionary computation, Deep Learning, Generative Models, Protein Design, Evolutionary Algorithms, Novel Proteins}, doi = {doi:10.1109/CEC55065.2022.9870421}, notes = {Also known as \cite{9870421}}, ) @INPROCEEDINGS(Bharti:2022:CEC, %xplor 24 Sep 2022 author = {Vandana Bharti and Bhaskar Biswas and Kaushal Kumar Shukla}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={{QL-SSA:} An Adaptive Q-Learning based Squirrel Search Algorithm for Feature Selection}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Machine learning techniques are widely used for discovering meaningful patterns and classifying real-world data. These datasets may be large and complex, so feature selection is the primary strategy for reducing the dimension of the data, with the general goal of reducing the amount of redundant and disruptive features in a dataset for fast and efficient data analysis without sacrificing significant predictive model performance. Due to exponentially high search space, feature selection is a complex optimization problem. It is practically impossible to evaluate all of the feature subsets manually. Nature-inspired optimization is widely used for this due to its inherent capability, and it solves feature selection tasks as a single objective optimization problem. However, the main issue is their frequent premature convergence, which results in an inadequate contribution to data mining. Even the majority of existing optimizers are not adaptive in nature. As a result, in this paper, we proposed QL-SSA, which combines Reinforcement Learning and the Squirrel Search Algorithm, making it more adaptive and robust for feature selection by maintaining a good balance between exploration and exploitation steps. It is tested on 20 real-world benchmark datasets using two classifiers, and the results show that it outperforms the baseline optimizer in most cases.}, keywords = {Q-learning, Data analysis, Evolutionary computation, Predictive models, Feature extraction, Search problems, Classification algorithms, Nature-inspired optimization, Reinforcement learning, Feature selection, Machine learning}, doi = {doi:10.1109/CEC55065.2022.9870311}, notes = {Also known as \cite{9870311}}, ) @INPROCEEDINGS(Miranda:2022:CEC, %xplor 24 Sep 2022 author = {Pericles B.C. Miranda and Rafael Ferreira Mello and Andre C.A. Nascimento and Tapas Si}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-Objective Optimization of Sampling Algorithms Pipeline for Unbalanced Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The sequencing of sampling algorithms has shown to be a promising approach in generating balanced versions of unbalanced data. Sequencing allows different algorithms of under-sampling and/or over-sampling to be performed in sequence, producing a resulting balanced database. However, defining the most appropriate sequence of sampling algorithms is challenging. This article treats the sequencing problem as a combinatorial optimization task and proposes a multi-objective optimization method to seek promising solutions that maximize the performance of classifiers both in accuracy and in F1-score. The results showed that the proposed method was capable of finding optimized sequences that improved the performance of the classifiers, obtaining statistically better results, mainly in F1- score, when compared with competing methods, in most of the selected unbalanced problems.}, keywords = {Sequential analysis, Databases, Pipelines, Optimization methods, Evolutionary computation, Classification algorithms, Task analysis, Sampling Algorithms, Multi-objective Optimization, Evolutionary Algorithms, Unbalanced Problems}, doi = {doi:10.1109/CEC55065.2022.9870435}, notes = {Also known as \cite{9870435}}, ) @INPROCEEDINGS(Sato:2022:CEC, %xplor 24 Sep 2022 author = {Yuji Sato and Tomoya Hirayama and Ryo Ikami}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Adaptive {PBI} for Massively Parallel {MOEA/D} in a Distributed Memory Environment}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper proposes an adaptive PBI for massively parallel MOEA/D in a distributed memory environment. Massively parallelization in a distributed memory environment effectively speeds up evolutionary multi-objective optimization algorithms for practical application problems. On the other hand, when MOEA/D is divided for parallelization by focusing on the reference vector in the objective function space, the T-neighbor is divided and the problem that the solution distribution becomes sparse near the boundary of the divided region arises. Here, we propose a method to improve the problem that the T-neighbor is divided and the solution distribution becomes sparse by adaptively controlling the penalty value in the PBI function according to the distance from the reference vector using a distribution function such as Laplace distribution. The effectiveness of the proposed method is shown by comparison with execution using a single CPU.}, keywords = {Focusing, Evolutionary computation, Aerospace electronics, Linear programming, Standards, Optimization, Distribution functions, MOEAlD, parallel and distributed processing, distributed memory environment, multi-objective evolutionary algorithms}, doi = {doi:10.1109/CEC55065.2022.9870272}, notes = {Also known as \cite{9870272}}, ) @INPROCEEDINGS(Mai:2022:CEC, %xplor 24 Sep 2022 author = {Sebastian Mai and Maximilian Deubel and Sanaz Mostaghim}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-Objective Roadmap Optimization for Multiagent Navigation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this paper, we investigate multi-objective opti-mization of roadmaps for multi-robot path planning. We propose a new representation for roadmaps based on polygons and explore its potentials on various scenarios. In addition, we define three objective functions to estimate the suitability of each roadmap for navigation, and propose a modification of the well-known NSGA-II algorithm to optimize the roadmaps. In our experiments, we compare the quality of the proposed optimized roadmaps with those based on regular grids. The results show that in complex environments with obstacles, the optimized roadmaps perform much more efficient than those on regular grids. In addition, the performance of the optimization can be significantly improved by using the regular grids to initialize the optimization process.}, keywords = {Runtime, Navigation, Scalability, Sociology, Focusing, Linear programming, Path planning, Multi-Objective Optimization, Multi-Robot Navigation, Roadmaps}, doi = {doi:10.1109/CEC55065.2022.9870300}, notes = {Also known as \cite{9870300}}, ) @INPROCEEDINGS(Seidelmann:2022:CEC, %xplor 24 Sep 2022 author = {Thomas Seidelmann and Sanaz Mostaghim}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Finding Cost-Effective Re-Layouting Solutions in Modern Brownfield Facility Layout Planning}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In facility layout planning (FLP) for shop floor optimization, the goal is to find an optimal arrangement for a number of machines in a given space. Despite the significance of machine selection for the FLP, the integration of arrangement and selection problems is neglected in the literature. This paper presents a methodology for solving an extended facility layout problem where not only the arrangement of machines is con-sidered, but also how many machines should be allocated and of which type. We assume the context of a modern brownfield planning project, where an initial layout already exists, which is to be re-purposed for manufacture of products with high variability. We apply our methodology in a case study where three objectives are to be minimized simultaneously. The results demonstrate that a standard NSGA-II introduces bias for this problem type that leads to an inadequate search space exploration. We test adapted mutation and crossover operators to overcome this challenge. While modified crossover had little impact, the new mutation operator was key to outperform the standard approach in all computed metrics. With this operator, we obtain diverse Pareto sets which strictly dominate up to 8percent of the solutions found by the standard approach on average.}, keywords = {Scheduling algorithms, Layout, Search problems, Minimization, Workstations, Planning, Space exploration, facility layout planning, job shop scheduling, brownfield, multi-objective optimization, evolutionary algorithm, mutation, crossover}, doi = {doi:10.1109/CEC55065.2022.9870345}, notes = {Also known as \cite{9870345}}, ) @INPROCEEDINGS(Hong:2022:CEC, %xplor 24 Sep 2022 author = {Haokai Hong and Min Jiang and Liang Feng and Qiuzhen Lin and Kay Chen Tan}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Large-scale multiobjective optimization problems (LSMOPs) refer to optimization problems with multiple con-flicting optimization objectives and hundreds or even thousands of decision variables. A key point in solving LSMOPs is how to balance exploration and exploitation so that the algorithm can search in a huge decision space efficiently. Large-scale multi-objective evolutionary algorithms consider the balance between exploration and exploitation from the individual's perspective. However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm. In this paper, we propose a large-scale multiobjective optimization algorithm based on the attention mechanism, called (LMOAM). The attention mechanism will assign a unique weight to each decision variable, and LMOAM will use this weight to strike a balance between exploration and exploitation from the decision variable level. Nine different sets of LSMOP benchmarks are conducted to verify the algorithm proposed in this paper, and the experimental results validate the effectiveness of our design.}, keywords = {Evolutionary computation, Benchmark testing, Optimization, Evolutionary algorithms, large-scale optimization, multiobjective optimization, attention mechanism}, doi = {doi:10.1109/CEC55065.2022.9870430}, notes = {Also known as \cite{9870430}}, ) @INPROCEEDINGS(Reuter:2022:CEC, %xplor 24 Sep 2022 author = {Julia Reuter and Manoj Cendrollu and Fabien Evrard and Sanaz Mostaghim and Berend {van Wachem}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Towards Improving Simulations of Flows around Spherical Particles Using Genetic Programming}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The simulation of particle-laden flows is a crucial task in fluid dynamics, requiring high computational cost owing to the complex interactions between numerous particles. Typically, the flow velocity is described with the equations proposed by Stokes. While there is an analytical solution for the Stokes flows around a single spherical particle, the Stokes flows around many particles are still unsolved. In this paper, we study Genetic Programming (GP) for symbolic regressions to explore the potentials of multi-objective GP in recovering analytical expressions for two and, in the future, N particles. We propose a new GP approach containing building blocks to scale up the problem and provide a new benchmark with 22 cases for this application. To identify the strengths and limitations of GP, we generate fully resolved training data from simulations. We compare the results of our algorithm to the superimposition method and a multi-layer perceptron as two baseline methods. The results show that GP can find comparable and sometimes better solutions with smaller failure rates than the two baseline methods. In addition, the produced solutions by GP are explainable and certain function patterns inline with physical laws can be identified across the benchmark problems.}, keywords = {Computational modeling, Fluid dynamics, Genetic programming, Training data, Evolutionary computation, Benchmark testing, Mathematical models, Genetic Programming, Stokes Flow, Explain-ability}, doi = {doi:10.1109/CEC55065.2022.9870301}, notes = {Also known as \cite{9870301}}, ) @INPROCEEDINGS(Said:2022:CEC, %xplor 24 Sep 2022 author = {Rihab Said and Maha Elarbi and Slim Bechikh and Carlos A. Coello Coello and Lamjed Ben Said}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Interval-based Cost-sensitive Classification Tree Induction as a Bi-level Optimization Problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Cost-sensitive learning is one of the most adopted approaches to deal with data imbalance in classification. Unfortunately, the manual definition of misclassification costs is still a very complicated task, especially with the lack of domain knowledge. To deal with the issue of costs' uncertainty, some researchers proposed the use of intervals instead of scalar values. This way, each cost would be delimited by two bounds. Nevertheless, the definition of these bounds remains as a very complicated and challenging task. Recently, some researches proposed the use of genetic programming to simultaneously build classification trees and search for optimal costs' bounds. As for any classification tree there is a whole search space of costs' bounds, we propose in this paper a bi-level evolutionary approach for interval-based cost-sensitive classification tree induction where the trees are constructed at the upper level while misclassification costs intervals bounds are optimized at the lower level. This ensures not only a precise evaluation of each tree but also an effective approximation of optimal costs intervals bounds. The performance and merits of our proposal are shown through a detailed comparative experimental study on commonly used imbalanced benchmark data sets with respect to several existing works.}, keywords = {Costs, Uncertainty, Genetic programming, Manuals, Evolutionary computation, Classification algorithms, Proposals, Cost-sensitive learning, misclassification costs' intervals, classification tree induction, bi-level optimization, evolutionary algorithms}, doi = {doi:10.1109/CEC55065.2022.9870424}, notes = {Also known as \cite{9870424}}, ) @INPROCEEDINGS(Nieto-Fuentes:2022:CEC, %xplor 24 Sep 2022 author = {Ricardo Nieto-Fuentes and Carlos Segura}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A replacement scheme based on dynamic penalization for controlling the diversity of the population in Genetic Programming}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Algorithms relating the amount of population's diversity to the elapsed period of execution have yielded important improvements. Particularly, schemes with a gradual shift from exploration to exploitation have excelled in several areas of Evolutionary Algorithms. A fairly recent method that applies this design principle is the Genetic Programming variant with Dynamic Management of Diversity (GP-DMD). GP-DMD applies a diversity-based replacement strategy that takes into account a user-defined function or policy that sets the amount of diversity desired in the population. Despite the improvements attained by GP-DMD, it is unable to precisely follow the user-defined policy in some cases. This calls into question its ability to perform a gradual shift from exploration to exploitation and hinders its extension to develop more complex dynamic and adaptive algorithms. This paper proposes the Genetic Programming variant with Controlled Dynamic Management of Diversity (GP-CDMD) which incorporates a novel replacement strategy that aims to improve its tracking capabilities. This is done through a probabilistic selection that takes into account the desired amount of diversity to restrict the diversity of the population. Results in the Symbolic Regression benchmark problem show a significant improvement in the tracking error, which results in features of the dynamics of the population that are more similar to the expected ones. This achievement facilitates the design of more complex diversity-based dynamic and adaptive optimizers and allows for better analyses on the implications of diversity in the GP area.}, keywords = {Heuristic algorithms, Sociology, Genetic programming, Adaptive algorithms, Evolutionary computation, Benchmark testing, Probabilistic logic, Genetic Programming, Diversity Management, Exploration, Intensification, Bloat}, doi = {doi:10.1109/CEC55065.2022.9870428}, notes = {Also known as \cite{9870428}}, ) @INPROCEEDINGS(Osaba:2022:CEC, %xplor 24 Sep 2022 author = {Eneko Osaba and Javier {Del Ser} and Aritz D. Martinez and Jesus L. Lobo}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Multifactorial Cellular Genetic Algorithm for Multimodal Multitask Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In multimodal optimization problems the main goal is to find as many global optima as possible by using a single search process. This type of optimization tasks emerges in many real-world scenarios in assorted fields including medicine, physics, and aerospace, among many others. However, addressing several multimodal optimization problems simultaneously has received little attention from the multitask optimization community to date. Even though solving different multimodal problems at the same time can largely benefit from the existing synergies among the modes of different tasks, this setup has been less studied than other optimization tasks. This work finds its inspiration in the incipient concepts of Evolutionary Multitasking and Multifactorial Optimization to propose a multifactorial Cellular Genetic Algorithm for solving multimodal optimization problems. Our designed algorithm expedites the search for the global optima of different problems at a time by including several algorithmic steps aimed at adapting the search itself as per the synergies found over the exploration of the problems' landscape. An extensive experimentation has been designed using 14 different functions from the CEC'2013 competition on multimodal optimization benchmark. Besides evaluating the performance of the devised algorithm to retain the global optima of every function in the benchmark, we also conduct an analysis of the transfer of knowledge among such functions. Finally, we compare its performance to that of a winning proposal in this CEC'2013 competition so as to reflect on the suitability of the multitasking paradigm to solve multimodal optimization tasks.}, keywords = {Evolutionary computation, Benchmark testing, Search problems, Multitasking, Proposals, Task analysis, Optimization, Multimodal optimization, Evolutionary Multitasking, Cellular Genetic Algorithm}, doi = {doi:10.1109/CEC55065.2022.9870324}, notes = {Also known as \cite{9870324}}, ) @INPROCEEDINGS(Ribeiro:2022:CEC, %xplor 24 Sep 2022 author = {Rafael Rodrigues Mendes Ribeiro and Carlos Dias Maciel}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={{AGAVaPS} - Adaptive Genetic Algorithm with Varying Population Size}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Recently there is great interest in optimization, especially on meta-heuristic algorithms. Many works have proposed improvements for these algorithms for general and specific applications. In this paper the Adaptive Genetic Algorithm with Varying Population Size (AGAVaPS) is proposed, an improvement of Genetic Algorithm. On the AGAVaPS each solution has their own mutation rate and number of iterations that the solution will be in the population. The proposed optimizer is tested against six other well established optimizers on the CEC2017 single objective optimization benchmark functions considering coverage of the search space and quality of solution obtained. It is also tested for feature selection and Bayesian network structural learning. The evolution of the population size over the iterations is also analysed. The results obtained show that the AGAVaPS has a very competitive performance in both, coverage and quality of solution.}, keywords = {Sociology, Metaheuristics, Evolutionary computation, Benchmark testing, Search problems, Feature extraction, Complexity theory, genetic algorithm, metaheuristic, optimization}, doi = {doi:10.1109/CEC55065.2022.9870394}, notes = {Also known as \cite{9870394}}, ) @INPROCEEDINGS(Wen:2022:CEC, %xplor 24 Sep 2022 author = {Chengxin Wen}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Hypervolume Approximation Method Based on Angular Point}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Hypervolume is one of the most widely used indicators in the field of evolutionary multiobjective optimization, whether for maintaining an external archive or environmental selection. However, the computational complexity of hypervolume in high dimensional space limits its application. Although a large number of hypervolume approximation methods have been proposed at present, most of them still have shortcomings in the trade-off between approximation accuracy and running time. To solve this problem, we propose an hypervolume approximation method based on angular points. The main idea is to find all the angular points of hypervolume region, and calculate the mean distance between the reference point and each angular point as the hypervolume approximation value. In the experimental part, six different spatial surfaces are used to test the accuracy of the algorithm, and four different dimensions are set for each type of surface. At the same time, two commonly used Hypervolume approximation methods were added for comparison. The results show that the proposed algorithm has better performance in most cases.}, keywords = {Monte Carlo methods, Shape, Evolutionary computation, Approximation algorithms, Search problems, Approximation methods, Computational complexity, Hypervolume approximation, Multi-objective optimization, Angular point}, doi = {doi:10.1109/CEC55065.2022.9870228}, notes = {Also known as \cite{9870228}}, ) @INPROCEEDINGS(Jiang:2022:CEC, %xplor 24 Sep 2022 author = {Yi Jiang and Chun-Hua Chen and Zhi-Hui Zhan and Yun Li and Jun Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Adversarial Differential Evolution for Multimodal Optimization Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Multimodal optimization problems (MMOPs) are sorts of optimization problems that have many global optima. To discover as many peaks as possible and increase the accuracy of the solutions, MMOP requires algorithms with great exploration and exploitation abilities. However, exploration and exploitation are in an adversarial relationship, since exploration aims to locate more optima via searching the global space rather than small regions, whereas exploitation targets to enhance the accuracy of solutions via searching in small areas. The key to efficiently solving MMOPs lies in striking a balance between exploration and exploitation. To achieve the goal, this paper proposes an adversarial differential evolution (ADE), containing an adversarial reproduction strategy and an adversarial selection strategy. Firstly, adversarial reproduction strategy generates offspring for exploration and offspring for exploitation and lets these two types of offspring compete for survival. Secondly, adversarial selection strategy employs a diversity-optimization-based selection and a crowding-based selection to select the offspring with both good diversity and good fitness. Diversity-optimization-based selection transforms the problem of selecting diverse individuals into an optimizing problem and solves it via an extra genetic algorithm to get the offspring with optimal diversity. Extensive experiments are conducted on CEC2013 MMOP benchmark to verify the effectiveness and efficiency of the proposed ADE. Experimental results show that ADE has advantages over the state-of-the-art MMOP algorithms.}, keywords = {Transforms, Evolutionary computation, Benchmark testing, Search problems, Optimization, Genetic algorithms, Multimodal Optimization, Differential Evolution, Evolutionary Computation, Adversarial Strategies}, doi = {doi:10.1109/CEC55065.2022.9870298}, notes = {Also known as \cite{9870298}}, ) @INPROCEEDINGS(Basher:2022:CEC, %xplor 24 Sep 2022 author = {Sheikh Faishal Basher and Brian J. Ross}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Managing Diversity and Many Objectives in Evolutionary Design}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={A new approach to evolving a diversity of high-quality solutions for problems having many objectives is presented. Mouret and Clune's MAP-Elites algorithm has been proposed as a way to evolve an assortment of diverse solutions to a problem. We extend MAP-Elites in a number of ways. We introduce into MAP-elites the many-objective strategy called sum-of-ranks, which enables problems with many objectives (4 and more) to be considered in the MAP. We also enhance MAP-Elites by extending it with multiple solutions per cell (the original MAP-Elites saves only a single solution per cell). Different ways of selecting cell members for reproduction are also considered. We test the new MAP-Elites strategies on the evolutionary art application of image generation. Using procedural textures, genetic programming is used with upwards of 15 light-weight image features to guide fitness. The goal is to evolve images that share image features with a given target image. Our experiments show that the new MAP-Elites algorithms produce a large number of diverse solutions, which can be competitive in quality to those from standard GP runs.}, keywords = {Visualization, Image synthesis, Image color analysis, Sociology, Search problems, Entropy, Performance analysis, Diversity, Genetic Programming, Many-objective Optimization, Evolutionary Art, Procedural Texture}, doi = {doi:10.1109/CEC55065.2022.9870353}, notes = {Also known as \cite{9870353}}, ) @INPROCEEDINGS(Santucci:2022:CEC, %xplor 24 Sep 2022 author = {Valentino Santucci and Marco Baioletti}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Fast Randomized Local Search for Low Budget Optimization in Black-Box Permutation Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Low budget black-box optimization is a relevant topic in many practical applications with expensive objective functions or tight real-time constraints. Recently, there has been a growing interest in addressing combinatorial permutation problems in a low budget and black-box scenario. In this context, most of the previously proposed algorithms learn a probabilistic model which guides the search by trying to somehow indicate the most effective areas of the permutation search space. However, the large size and the inherent discontinuity of the permutation space may lessen the effectiveness of this approach when a low, or very low, budget of evaluations is considered. Moving from this consideration, in this work we present a simpler elitist trajectory-based algorithm for low budget black-box optimization of permu-tation problems. The proposed algorithm, namely FAT-RLS, is based on three core ideas: a randomized local search scheme, an adaptive perturbation strength and the use of a tabu structure. A series of experiments held on commonly adopted benchmark problems clearly shows that FAT-RLS obtains better or compara-ble effectiveness with respect to the previous proposals. Moreover, its negligible computational overhead is of particular interest in mission critical situations where tight real-time constraints have to be matched.}, keywords = {Perturbation methods, Mission critical systems, Evolutionary computation, Search problems, Probabilistic logic, Linear programming, Real-time systems, low budget optimization, optimization under real-time constraints, black-box permutation problems, randomized local search}, doi = {doi:10.1109/CEC55065.2022.9870328}, notes = {Also known as \cite{9870328}}, ) @INPROCEEDINGS(Richoux:2022:CEC, %xplor 24 Sep 2022 author = {Florian Richoux}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Terrain Analysis in {StarCraft} 1 and 2 as Combinatorial Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Terrain analysis in Real-Time Strategy games is a necessary step to allow spacial reasoning. The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial representation. On StarCraft games, all previous works on terrain analysis propose a crisp analysis based on connected component detection, Voronoi diagram computation and pruning, and region merging. Those methods have been implemented as game-specific libraries, and they can only offer the same kind of analysis for all maps and all users. In this paper, we propose a way to consider terrain analysis as a combinatorial optimization problem. Our method allows different kinds of analysis by changing constraints or the objective function in the problem model. We also present a library, Taunt, implementing our method and able to handle both StarCraft 1 and StarCraft 2 maps. This makes our library a universal tool for StarCraft bots with different spatial representation needs. We believe our library unlocks the possibility to have real adaptive AIs playing StarCraft, and can be the starting point of a new wave of bots.}, keywords = {Analytical models, Merging, Games, Evolutionary computation, Linear programming, Libraries, Real-time systems, Terrain Analysis, Real-Time Strategy Game, StarCraft, Combinatorial Optimization, Constraint Program-ming}, doi = {doi:10.1109/CEC55065.2022.9870230}, notes = {Also known as \cite{9870230}}, ) @INPROCEEDINGS(Chang:2022:CEC, %xplor 24 Sep 2022 author = {Yatong Chang and Wenjian Luo and Xin Lin and Zeneng She and Yuhui Shi}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multiparty Multiobjective Optimization By {MOEA/D}}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={As a special class of multiobjective optimization problems (MOPs), multiparty multiobjective optimization prob-lems (MPMOPs) widely exist in real-world applications. In MPMOPs, there are multiple decision makers (DMs) concerning multiple different conflicting objectives. The goal of solving MPMOPs is to catch the best solutions satisfying all DMs as far as possible. To our best knowledge, there is little attention on solving MPMOPs, and only two optimization algorithms, i.e., OptMPNDS and OptMPNDS2, are proposed. These two algorithms are both based on non-dominated sorting genetic algorithm II (NSGA-II). However, there is no algorithm pro-posed from the decomposition perspective to solve MPMOPs. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a popular multiobjective evolutionary optimization algorithm for MOPs. In this paper, we embed the party-by-party strategy into MOEA/D and propose the novel optimization algorithm MOEA/D-MP to solve MPMOPs. The experimental results on the benchmarks have demonstrated the effectiveness of MOEA/D-MP.}, keywords = {Evolutionary computation, Benchmark testing, Optimization, Sorting, Genetic algorithms, Multiparty multiobjective optimization, multi-objective optimization, decomposition, evolutionary algorithms}, doi = {doi:10.1109/CEC55065.2022.9870294}, notes = {Also known as \cite{9870294}}, ) @INPROCEEDINGS(Neri:2022:CEC, %xplor 24 Sep 2022 author = {Ferrante Neri and Matthew Todd}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Study on Six Memetic Strategies for Multimodal Optimisation by Differential Evolution}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper presents an experimental study on memetic strategies to enhance the performance of population-based metaheuristics for multimodal optimisation. The purpose of this work is to devise some recommendations about algorithmic design to allow a successful combination of local search and niching techniques. Six memetic strategies are presented and tested over five population-based algorithms endowed with niching techniques. Experimental results clearly show that local search enhances the performance of the framework for multimodal optimisation in terms of both peak ratio and success rate. The most promising results are obtained by the variants that employ an archive that pre-selects the solutions undergoing local search thus avoiding computational waste. Furthermore, promising results are obtained by variants that reduce the exploitation pressure of the population-based framework by using a simulated annealing logic in the selection process, leaving the exploitation task to the local search.}, keywords = {Memetics, Three-dimensional displays, Sociology, Metaheuristics, Simulated annealing, Evolutionary computation, Market research, memetic algorithms, multimodal optimisation, differential evolution, niching, BFGS}, doi = {doi:10.1109/CEC55065.2022.9870221}, notes = {Also known as \cite{9870221}}, ) @INPROCEEDINGS(Alcaraz-Herrera:2022:CEC, %xplor 24 Sep 2022 author = {Hugo Alcaraz-Herrera and John Cartlidge}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Exploration of Ontological Representations for Evolutionary Computation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This research explores the utility of ontological representations using object-oriented (OO) design principles, such that characteristics of the problem domain are directly mapped onto the representation of individuals. A comparison against more traditional representations is performed in two problem domains of differing complexity: (i) Tangram, a simple geometric puzzle; and (ii) EvoRecSys, an evolutionary recommender system for health and well-being advice. We show that OO representations aid research and development as naturally decoupled components can be more easily modified and extended, which can in turn lead to the discovery of better solutions.}, keywords = {Codes, Shape, Evolutionary computation, Games, Complexity theory, Task analysis, Recommender systems, Object-Oriented Paradigm, Evolutionary Computation, Representation strategy, Genetic Algorithms}, doi = {doi:10.1109/CEC55065.2022.9870376}, notes = {Also known as \cite{9870376}}, ) @INPROCEEDINGS(Kaucic:2022:CEC, %xplor 24 Sep 2022 author = {Massimiliano Kaucic and Filippo Piccotto}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A level-based learning swarm optimizer with a hybrid constraint-handling technique for large-scale portfolio selection problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper proposes a variant of the dynamic level-based learning swarm optimizer algorithm for solving large-scale constrained portfolio optimization problems. More specifically, we aim to maximize the inner rate of risk aversion, a recently proposed performance measure that incorporates higher moments of the portfolio return distribution and disaster risk. Our portfolio design includes cardinality, box, and budget constraints; an upper bound for the portfolio turnover maintains a control on the transaction costs during rebalancing phases. The algorithm uses a compressed coding scheme to encode the dependent variables into continuous ones to handle portfolio cardinalities. A repair operator deals with box and budget constraints, and an adaptive penalty function approach is used for the turnover constraint. The profitability of the developed investment strategy is tested using data from the MSCI World Index. The out-of-sample results show that our approach can consistently outperform the benchmark index and the cardinality constrained mean-variance model.}, keywords = {Upper bound, Profitability, Heuristic algorithms, Benchmark testing, Loss measurement, Encoding, Indexes, large-scale portfolio optimization, inner rate of risk aversion, cardinality, turnover, dynamic level-based learning swarm optimizer, hybrid constraint-handling}, doi = {doi:10.1109/CEC55065.2022.9870358}, notes = {Also known as \cite{9870358}}, ) @INPROCEEDINGS(Thymianis:2022:CEC, %xplor 24 Sep 2022 author = {Marios Thymianis and Alexandros Tzanetos and Eneko Osaba and Georgios Dounias and Javier {Del Ser}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Electric Vehicle Routing Problem: Literature Review, Instances and Results with a Novel Ant Colony Optimization Method}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={One of the most well-known problems in combinatorial optimization is the Vehicle Routing Problem (VRP). Significant research has been done around this problem in two different perspectives: investigating new solving approaches, and studying variants of VRP which take into consideration multiple restrictions and constraints. One of such versions is the Electric Vehicle Routing Problem (EVRP), whose main objective is to find the optimal route of a fleet of electric vehicles, taking into account the locations of charging stations and the battery consumption of the mobile units. The aim of this study is threefold: (a) to perform a brief literature review on meta-heuristic approaches applied to the EVRP, (b) to offer insights on the available data instances for this problem, and (c) to discuss on the results of an experimental benchmark aimed at comparing different meta-heuristic approaches over diverse EVRP instances, including the proposal and evaluation of a novel Ant Colony Optimization approach.}, keywords = {Ant colony optimization, Systematics, Bibliographies, Metaheuristics, Vehicle routing, Benchmark testing, Routing, Meta-heuristics, Vehicle Routing Problem, Electric Vehicle Routing Problem, Ant Colony Optimization}, doi = {doi:10.1109/CEC55065.2022.9870373}, notes = {Also known as \cite{9870373}}, ) @INPROCEEDINGS(Blank:2022:CEC, %xplor 24 Sep 2022 author = {Julian Blank and Kalyanmoy Deb}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Parameter Tuning and Control: A Case Study on Differential Evolution With Polynomial Mutation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Metaheuristics are known to be effective for solving a broad category of optimization problems. However, most heuristics require different parameter settings appropriately for a problem class or even for a specific problem. Researchers address this commonly by performing a parameter tuning study (also known as hyper-parameter optimization) or developing a parameter control mechanism that changes parameters dynamically. Whereas parameter tuning is computationally expensive and limits the parameter configuration to stay constant throughout the run, parameter control is also a challenging task because all dynamics induced by various operators must be learned to make an appropriate adaptation of parameters on the fly. This paper investigates parameter tuning and control for a well-known optimization method - differential evolution (DE). In contrast to most existing DE practices, an additional individualistic evolutionary operator called polynomial mutation is incorporated into the offspring creation. Results on test problems with up to 50 variables indicate that mutation can be helpful for multi-modal problems to escape from local optima. On the one hand, the effectiveness of parameter tuning for a specific problem becomes apparent; on the other hand, its generalization capabilities seem to be limited. Moreover, a generic coevolutionary approach for parameter control outperforms a random choice of parameters. Recognizing the importance of choosing a suitable parameter configuration to solve any optimization problem, we have incorporated a standard implementation of both tuning and control approaches into a single framework, providing a direction for the evolutionary computation and optimization researchers to use and further investigate the effects of parameters on DE and other metaheuristics-based algorithms.}, keywords = {Metaheuristics, Optimization methods, Evolutionary computation, Task analysis, Tuning, Standards, Parameter Tuning, Parameter Control, Differential Evolution, Self-Adaptive, Co-Evolution, Metaheuristics}, doi = {doi:10.1109/CEC55065.2022.9870219}, notes = {Also known as \cite{9870219}}, ) @INPROCEEDINGS(Rypesc:2022:CEC, %xplor 24 Sep 2022 author = {Grzegorz Rypesc and Grzegorz Kurzejamski and Jacek Komorowski}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Sports Camera Pose Refinement Using an Evolution Strategy}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper presents a robust end-to-end method for sports cameras extrinsic parameters optimization using a novel evolution strategy. First, we developed a neural network ar-chitecture for an edge or area-based segmentation of a sports field. Secondly, we implemented the evolution strategy, which purpose is to refine extrinsic camera parameters given a single, segmented sports field image. Experimental comparison with state-of-the-art camera pose refinement methods on real-world data demonstrates the superiority of the proposed algorithm. We also perform an ablation study and propose a way to generalize the method to additionally refine the intrinsic camera matrix.}, keywords = {Image segmentation, Image edge detection, Neural networks, Evolutionary computation, Cameras, Calibration, Optimization, pose refinement, camera calibration, computer vision, image segmentation, evolution strategy}, doi = {doi:10.1109/CEC55065.2022.9870256}, notes = {Also known as \cite{9870256}}, ) @INPROCEEDINGS(Abramowitz:2022:CEC, %xplor 24 Sep 2022 author = {Sasha Abramowitz and Geoff Nitschke}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Towards Run-time Efficient Hierarchical Reinforcement Learning}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper investigates a novel method combining Scalable Evolution Strategies (S-ES) and Hierarchical Reinforcement Learning (HRL). S-ES, named for its excellent scalability, was popularised with demonstrated performance comparable to state-of-the-art policy gradient methods. However, S-ES has not been tested in conjunction with HRL methods, which empower temporal abstraction thus allowing agents to tackle more challenging problems. We introduce a novel method merging S-ES and HRL, which creates a highly scalable and efficient (compute time) algorithm. We demonstrate that the proposed method benefits from S-ES's scalability and indifference to delayed rewards. This results in our main contribution: significantly higher learning speed and competitive performance compared to gradient-based HRL methods, across a range of tasks.}, keywords = {Gradient methods, Scalability, Merging, Reinforcement learning, Evolutionary computation, Computational efficiency, Task analysis, Hierarchical Reinforcement Learning, Evolution Strategies, Ant Gather, Ant Maze, Ant Push}, doi = {doi:10.1109/CEC55065.2022.9870368}, notes = {Also known as \cite{9870368}}, ) @INPROCEEDINGS(Peng:2022:CEC, %xplor 24 Sep 2022 author = {Wei Peng and Yue Hu and Yuqiang Xie and Luxi Xing and Yajing Sun}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={{CogIntAc:} Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspective}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Intention, emotion and action are important psychological factors in human activities, which play an important role in the interaction between individuals. How to model the interaction process between individuals by analyzing the relationship of their intentions, emotions, and actions at the cognitive level is challenging. In this paper, we propose a novel cognitive framework of individual interaction. The core of the framework is that individuals achieve interaction through external action driven by their inner intention. Based on this idea, the interactions between individuals can be constructed by establishing relationships between the intention, emotion and action. Furthermore, we conduct analysis on the interaction between individuals and give a reasonable explanation for the predicting results. To verify the effectiveness of the framework, we reconstruct a dataset and propose three tasks as well as the corresponding baseline models, including action abduction, emotion prediction and action generation. The novel framework shows an interesting perspective on mimicking the mental state of human beings in cognitive science.}, keywords = {Analytical models, Computational modeling, Psychology, Evolutionary computation, Predictive models, Cognitive science, Task analysis, action abduction, emotion prediction, action generation, interaction}, doi = {doi:10.1109/CEC55065.2022.9870410}, notes = {Also known as \cite{9870410}}, ) @INPROCEEDINGS(Liu:2022:CEC, %xplor 24 Sep 2022 author = {Wei Liu and Li Chen and Xingxing Hao and Fei Xie and Haiyang Nan and Honghao Zhai and Jiyao Yang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A two-stage multi-objective evolutionary algorithm for large-scale multi-objective optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In large-scale multi-objective problems, the traditional offspring generation operators is directionless and blind, which leads to the low searching capability in a huge search space. For this, we propose a two-stage multi-objective evolutionary algorithm, named MOEA-BTS to solve large-scale multi-objective problems(LSMOPs). In MOEA-BTS, the offspring generation process is divided into two stages. In the early stage, a new hybrid of local and global search direction construction method is proposed, aiming to balance the exploitation and exploration of the search. In the late stage, a series of weight vectors divide the decision space into subspaces, where the competitive swarm optimization algorithm is performed for further precise optimizations. Experiments are conducted on the LSMOPs with 500 and 1000 decision variables and results demonstrate that our proposed algorithm can perform better than several state-of-the-art evolutionary algorithms.}, keywords = {Benchmark testing, Search problems, Particle swarm optimization, Optimization, evolutionary algorithm, multi-objective opti-mization, large-scale optimization, directed sampling, competitive swarm optimizer (CSO)}, doi = {doi:10.1109/CEC55065.2022.9870333}, notes = {Also known as \cite{9870333}}, ) @INPROCEEDINGS(Nomura:2022:CEC, %xplor 24 Sep 2022 author = {Masahiro Nomura and Isao Ono}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Fast Moving Natural Evolution Strategy for High-Dimensional Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this work, we propose a new variant of natural evolution strategies (NES) for high-dimensional black-box opti-mization problems. The proposed method, CR-FM-NES, extends a recently proposed state-of-the-art NES, Fast Moving Natural Evolution Strategy (FM-NES), in order to be applicable in high-dimensional problems. CR-FM-NES builds on an idea using a restricted representation of a covariance matrix instead of using a full covariance matrix, while inheriting an efficiency of FM-NES. The restricted representation of the covariance matrix enables CR-FM-NES to update parameters of a multivariate normal distribution in linear time and space complexity, which can be applied to high-dimensional problems. Our experimental results reveal that CR-FM-NES does not lose the efficiency of FM-NES, and on the contrary, CR-FM-NES has achieved significant speedup compared to FM-NES on some benchmark problems. Furthermore, our numerical experiments using 200, 600, and 1000-dimensional benchmark problems demonstrate that CR-FM-NES is effective over scalable baseline methods, VD-CMA and Sep-CMA.}, keywords = {Machine learning algorithms, Sociology, Stochastic processes, Machine learning, Evolutionary computation, Switches, Benchmark testing, Natural Evolution Strategies, Black-Box Opti-mization, High Dimension}, doi = {doi:10.1109/CEC55065.2022.9870206}, notes = {Also known as \cite{9870206}}, ) @INPROCEEDINGS(Megane:2022:CEC, %xplor 24 Sep 2022 author = {Jessica Megane and Nuno Lourenco and Penousal Machado}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Probabilistic Structured Grammatical Evolution}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The grammars used in grammar-based Genetic Programming (GP) methods have a significant impact on the quality of the solutions generated since they define the search space by restricting the solutions to its syntax. In this work, we propose Probabilistic Structured Grammatical Evolution (PSGE), a new approach that combines the Structured Grammatical Evolution (SGE) and Probabilistic Grammatical Evolution (PGE) representation variants and mapping mechanisms. The genotype is a set of dynamic lists, one for each non-terminal in the grammar, with each element of the list representing a probability used to select the next Probabilistic Context-Free Grammar (PCFG) derivation rule. PSGE statistically outperformed Grammatical Evolution (GE) on all six benchmark problems studied. In comparison to PGE, PSGE outperformed 4 of the 6 problems analyzed.}, keywords = {Sociology, Production, Benchmark testing, Syntactics, Germanium, Probabilistic logic, Search problems, Grammatical Evolution, Grammar-based Genetic Programming, Grammar Design, Probabilistic}, doi = {doi:10.1109/CEC55065.2022.9870397}, notes = {Also known as \cite{9870397}}, ) @INPROCEEDINGS(Takahashi:2022:CEC, %xplor 24 Sep 2022 author = {Kenjiro Takahashi and Yoshikazu Fukuyama and Shuhei Kawaguchi and Takaomi Sato}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Optimal Production Scheduling using a Production Simulator and Multi-population Global-best Modified Brain Storm Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper proposes an optimal production scheduling method using the production simulator and multi-population global-best modified brain storm optimization (MP-GMBSO). Currently, in industry sector, decarbonization and carbon neutrality are approached by technical innovations such as Industry 4.0. In particular, optimal production scheduling researches which are important in production environments have been conducted actively. However, there is a gap between the previous optimal production scheduling researches and production schedule generating methods of practical production environments. The proposed method can fill the gap and it can be applied to the practical production environments. Results of the proposed method are compared with those of the conventional MBSO [7] and GMBSO based methods. It is verified that the proposed MP-GMBSO based method can find higher quality production schedules. In addition, it is verified that there is a significant difference among the conventional MBSO and GMBSO based methods, and the proposed MP-GMBSO based method with 0.05 significant level by the Friedman test as a priori test and the Wilcoxon signed rank test with Bonferroni-Holm correction as a post hoc test. In addition, the objective function of the target production scheduling has needles and it is found that the problem is one of the challenging problems to be optimized. The proposed MP-GMBSO based method can solve the problem better than the conventional MBSO and GMBSO based methods even with the challenging characteristic of the problem.}, keywords = {Schedules, Technological innovation, Job shop scheduling, Storms, Processor scheduling, Production, Low-carbon economy, decarbonization, optimal production scheduling, production simulator, multi-population global-best modified brain storm optimization}, doi = {doi:10.1109/CEC55065.2022.9870309}, notes = {Also known as \cite{9870309}}, ) @INPROCEEDINGS(Mitsui:2022:CEC, %xplor 24 Sep 2022 author = {Yasuyuki Mitsui and Yuki Yamakoshi and Hiroyuki Sato}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Real-world Item Stock Allocation for Japanese Electric Commerce}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This work addresses a real-world item stock allocation using evolutionary optimization for Japanese electric-commerce. We use the actual data of items to be ordered, existing warehouses, and order records from customers. The target area is all over Japan. The task is to find the optimal distribution of one thousand items to eight warehouses. The problem has two objectives: minimizing the total shipping cost and minimizing the average number of stocked warehouses. The problem also has constraints, including the warehouse capacities and the maximum possible number of shipping from each warehouse. Since the commonly used uniform crossover tends to be destructive in this problem, we propose four crossovers for the problem: the item, the warehouse, the item uniform, the warehouse uniform crossovers. Experimental results show that the proposed item crossover is suited to solve this problem, and the obtained item stock allocations can significantly reduce shipping and stocking costs compared with a human-made allocation.}, keywords = {Costs, Evolutionary computation, Computational efficiency, Resource management, Electronic commerce, Task analysis, Optimization, real-world optimization, item stock allocation, multi-objective optimization, combinatorial optimization, computationally expensive optimization, evolutionary algorithm, semantic crossover}, doi = {doi:10.1109/CEC55065.2022.9870390}, notes = {Also known as \cite{9870390}}, ) @INPROCEEDINGS(Fukuhara:2022:CEC, %xplor 24 Sep 2022 author = {Kohei Fukuhara and Ryo Kumagai and Fukawa Yuta and Tanabe Shin-ichi and Hiroki Kawano and Yoshihiro Ohta and Hiroyuki Sato}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Digital Twin Based Evolutionary Building Facility Control Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This work addresses a real-world building facility control problem by using evolutionary algorithms. The variables are facility control parameters, such as the start/stop time of air-conditioning, lighting, and ventilation operation, etc. The problem has six objectives: annual energy consumption, elec-tricity cost, overall satisfaction, thermal satisfaction, indoor air quality satisfaction, and lighting satisfaction. The problem has five constraints: power consumption, temperature, humidity, $\mathbf{CO}_{2}$ concentration, and average illuminance. To solve the problem, we utilize IBEA framework. For efficient solution generation, we employ the steady-state model for IBEA. We propose the total constraint win-loss rank for multiple constraints to treat multiple constraints equally. Experimental results on artificial test problems and building facility control problems show that the proposed constraint IBEA with steady-state and total con-straint win-loss rank archives better search performance than conventional representative algorithms.}, keywords = {Costs, Buildings, Sociology, Lighting, Evolutionary computation, Search problems, Ventilation, building facility control, multi-objective opti-mization, evolutionary algorithm, constraint handling technique}, doi = {doi:10.1109/CEC55065.2022.9870207}, notes = {Also known as \cite{9870207}}, ) @INPROCEEDINGS(Yu:2022:CEC, %xplor 24 Sep 2022 author = {Yongbo Yu and Tao Shi and Hui Ma and Gang Chen}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Genetic Programming-Based Hyper-Heuristic Approach for Multi-Objective Dynamic Workflow Scheduling in Cloud Environment}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={With the popularity of cloud computing, many organizations process their workflow tasks in cloud resources based on the Pay-As-Per-Use model. Dynamic Workflow Scheduling (DWS) aims to allocate dynamically arriving workflow tasks to cloud resources with optimal makespan, cost, load-balancing, etc. To timely allocate arriving tasks, heuristics have been used to solve the DWS problem in cloud environment. However, most of them are manually designed, considering a single objective, and use simple features to allocate resources to workflow tasks. In practice, multiple objectives should be considered to provide trade-off heuristics for users to choose from. In this paper, we propose a genetic programming hyper-heuristic (GPHH) approach to automatically generate multiple heuristics for multi-objective DWS. Our experimental evaluation using benchmark datasets demonstrates the effectiveness of our proposed GPHH approach.}, keywords = {Cloud computing, Costs, Processor scheduling, Computational modeling, Genetic programming, Organizations, Evolutionary computation, Cloud computing, dynamic workflow scheduling, genetic programming, multi-objective, GPHH}, doi = {doi:10.1109/CEC55065.2022.9870403}, notes = {Also known as \cite{9870403}}, ) @INPROCEEDINGS(Nimura:2022:CEC, %xplor 24 Sep 2022 author = {Naruhiko Nimura and Akira Oyama}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Topology Optimization Using Quadtree Genetic Programming}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={A new topology optimization method using genetic programming is proposed. To simultaneously achieve the gen-eration of shapes with high degrees of freedom and efficient optimization, the quadtree used in image processing is employed to reduce the number of design variables. Because the quadtree used in image processing implicitly holds coordinate information, we propose a new crossover and mutation method that inherits this information. For validation of the proposed approach, shape optimization and topology optimization are demonstrated where target airfoils including multi-element airfoils are reproduced. As a result, it is confirmed that the proposed method works for shape and topology optimizations with high efficiency.}, keywords = {Shape, Image processing, Genetic programming, Optimization methods, Evolutionary computation, Topology, Optimization, Genetic programming, quadtree, topology opti-mization}, doi = {doi:10.1109/CEC55065.2022.9870331}, notes = {Also known as \cite{9870331}}, ) @INPROCEEDINGS(Tian:2022:CEC, %xplor 24 Sep 2022 author = {Ye Tian and Haowen Chen and Xiaoshu Xiang and Hao Jiang and Xingyi Zhang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Comparative Study on Evolutionary Algorithms and Mathematical Programming Methods for Continuous Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Evolutionary algorithms and mathematical programming methods are currently the most popular optimizers for solving continuous optimization problems. Owing to the population based search strategies, evolutionary algorithms can find a set of promising solutions without using any problem-specific information. By contrast, with the assistance of gradient and other information of the functions, mathematical programming methods can quickly converge to a single optimum. While these two types of optimizers have their own advantages and disadvantages, the performance comparison between them is rarely touched. It is known that gradient descent methods generally converge faster than evolutionary algorithms, but when can evolutionary algorithms outperform gradient descent methods? How is the scalability of them? To answer these questions, this paper first gives a review of popular evolutionary algorithms and mathematical programming methods, then conducts several experiments to compare their performance from various aspects, and finally draws some conclusions.}, keywords = {Couplings, Training, Scalability, Sociology, Evolutionary computation, Search problems, Time complexity}, doi = {doi:10.1109/CEC55065.2022.9870359}, notes = {Also known as \cite{9870359}}, ) @INPROCEEDINGS(Roy:2022:CEC, %xplor 24 Sep 2022 author = {Nicolas Roy and Charlotte Beauthier and Alexandre Mayer}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Setup of a New Adaptive Fuzzy Particle Swarm Optimization Algorithm}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Heuristic optimization methods such as Particle Swarm Optimization (PSO) depend on their parameters to achieve good performance on a given class of problems. Some modifications of heuristic algorithms aim to adapt those parameters during the optimization process. We present a framework to design such adaptation strategies using continuous fuzzy feedback control. Our framework, which is not tied to a particular algorithm, provides us with a simple interface where probes are sampled in the optimization process and parameters are fed back. The process of turning probes into parameters uses fuzzy logic rule sets, where the design of rules aims to maximize performance on a training benchmark. This meta-optimization is achieved by a Bayesian Optimizer (BO) with a Gradient Boosted Regression Trees (GBRT) prior. The robustness of the control is also assessed on a validation benchmark.}, keywords = {Training, Systematics, Prototypes, Optimization methods, Benchmark testing, Turning, Robustness, PSO, Systematic Algorithm Design, Fuzzy Control, Swarm Intelligence, Hyperheuristics}, doi = {doi:10.1109/CEC55065.2022.9870387}, notes = {Also known as \cite{9870387}}, ) @INPROCEEDINGS(Liu:2022:CEC, %xplor 24 Sep 2022 author = {Songbai Liu and Min Jiang and Qiuzhen Lin and Kay Chen Tan}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Large-Scale Multiobjective Optimization via Self-guided Problem Transformation}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The performance of traditional multiobj ective evolutionary algorithms (MOEAs) often deteriorates rapidly when using them to solve large-scale multiobjective optimization problems (LMOPs). To effectively handle LMOPs, we propose a large-scale MOEA via self-guided problem transformation. In the proposed optimizer, the original large-scale search space is transferred to a lower-dimensional weighted space by the guidance of solutions themselves, aiming to effectively search in the weighted space for speeding up the convergence of the population. Specifically, the variables of the target LMOP are adaptively and randomly divided into multiple equal groups, and then solutions are self-guided to construct the small-scale weighted space correspondingly to these variable groups. In this way, each solution is projected as a self-guided vector with multiple weight variables, and then new weight vectors can be generated by searching in the weighted space. Next, new offspring is produced by inversely mapping the newly generated weight vectors to the original search space of this LMOP. Finally, the proposed optimizer is tested on two different LMOP test suites by comparing them with five competitive large-scale MOEAs. Experimental results show some advantages of the proposed algorithm in solving the considered benchmarks.}, keywords = {Sociology, Evolutionary computation, Benchmark testing, Search problems, Multitasking, Distance measurement, Resource management, Evolutionary Algorithm, Self-Guided Problem Transformation, Large-Scale Multiobjective Optimization}, doi = {doi:10.1109/CEC55065.2022.9870259}, notes = {Also known as \cite{9870259}}, ) @INPROCEEDINGS(Almansoori:2022:CEC, %xplor 24 Sep 2022 author = {Ahmed Almansoori and Muhanad Alkilabi and Elio Tuci}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Comparative Study on Decision Making Mechanisms in a Simulated Swarm of Robots}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={A swarm of robots can collectively select an option among the available alternatives offered by the environment through a process known as collective decision-making. This process is characterised by the fact that once a decision is made by the group, it can not be attributed to any of its group members. In the large majority of the swarm robotics literature, only a few types of mechanisms have been used to allow the robots of a swarm to make collective decisions. Namely, the mechanisms referred to as the Voter and the Majority model or variations of these two models. Recently, an alternative solution has been proposed which is based on the use of dynamical neural networks as individual decision-making mechanisms. This alternative solution proved effective in a perceptual discrimination task. In this paper, we carry out extensive comparative tests that quantitatively evaluate, on a perceptual discrimination task, the Voter model and the dynamic neural network model on a variety of operating conditions and for swarms that differ in their size. The results of our study clearly indicate that the performances of a swarm employing dynamical neural networks as the decision-making mechanism are more robust, more adaptable to a dynamic environment, and more scalable to a larger swarm size than the performances of a swarm employing the Voter model as the decision-making mechanism. We account for these performance differences with an analysis of the two models.}, keywords = {Adaptation models, Computational modeling, Scalability, Decision making, Swarm robotics, Artificial neural networks, Evolutionary computation, Swarm robotics, Collective decision making, automatic design}, doi = {doi:10.1109/CEC55065.2022.9870208}, notes = {Also known as \cite{9870208}}, ) @INPROCEEDINGS(Xu:2022:CEC, %xplor 24 Sep 2022 author = {Mingyu Xu and Yongjin Zheng and Yew-Soon Ong and Zexuan Zhu and Xiaoliang Ma}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A multifactorial differential evolution with hybrid global and local search strategies}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Evolutionary multitasking optimization (EMTO) solves multiple optimization tasks meanwhile in the framework of evolutionary algorithm, aiming at improving the solving performance on each task via knowledge transfer among tasks. As one of the representative EMTO algorithms, multifactorial evolutionary algorithm (MFEA) has attracted great attention and has been used to solve many optimization problems. However, most of MFEAs tend to suffer from premature convergence. To deal with this issue, this article designs a novel MFEA by integrating differential evolution, and a hybrid of global and local search strategies, named MFDE-GLS for short. Particularly, the global search strategy is based on an opposition-based learning and a Gaussian perturbation to improve the search ability and maintain population diversity. A local search strategy is introduced by combining 1-dimension search and n-dimension search to accelerate the convergence. Moreover, a new environmental selection mechanism is developed to keep the elite individuals while maintaining the population diversity based on the affinity propagation clustering method. Comprehensive experiments were conducted on both single-objective and multi-objective multi-task benchmark problems to show the effectiveness of the proposed algorithm.}, keywords = {Heuristic algorithms, Sociology, Clustering algorithms, Evolutionary computation, Search problems, Multitasking, Task analysis, Evolutionary algorithms, multi-task optimization MFEA, knowledge transfer}, doi = {doi:10.1109/CEC55065.2022.9870335}, notes = {Also known as \cite{9870335}}, ) @INPROCEEDINGS(Valentim:2022:CEC, %xplor 24 Sep 2022 author = {Ines Valentim and Nuno Lourenco and Nuno Antunes}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Adversarial Robustness Assessment of {NeuroEvolution} Approaches}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={NeuroEvolution automates the generation of Artificial Neural Networks through the application of techniques from Evolutionary Computation. The main goal of these approaches is to build models that maximize predictive performance, some-times with an additional objective of minimizing computational complexity. Although the evolved models achieve competitive results performance-wise, their robustness to adversarial examples, which becomes a concern in security-critical scenarios, has received limited attention. In this paper, we evaluate the adversarial robustness of models found by two prominent Neu-roEvolution approaches on the CIFAR-10 image classification task: DENSER and NSGA-Net. Since the models are publicly available, we consider white-box untargeted attacks, where the perturbations are bounded by either the $L_{2}$ or the $L_{\infty}$ -norm. Similarly to manually-designed networks, our results show that when the evolved models are attacked with iterative methods, their accuracy usually drops to, or close to, zero under both distance metrics. The DENSER model is an exception to this trend, showing some resistance under the $L_{2}$ threat model, where its accuracy only drops from 93.7percent to 18.1percent even with iterative attacks. Additionally, we analyzed the impact of pre-processing applied to the data before the first layer of the network. Our observations suggest that some of these techniques can exacerbate the perturbations added to the original inputs, potentially harming robustness. Thus, this choice should not be neglected when automatically designing networks for applications where adversarial attacks are prone to occur.}, keywords = {Resistance, Measurement, Computational modeling, Perturbation methods, Evolutionary computation, Predictive models, Market research, Adversarial Examples, Convolutional Neural Networks, NeuroEvolution, Robustness}, doi = {doi:10.1109/CEC55065.2022.9870202}, notes = {Also known as \cite{9870202}}, ) @INPROCEEDINGS(Delgado-Osuna:2022:CEC, %xplor 24 Sep 2022 author = {Jose A. Delgado-Osuna and Carlos Garcia-Martinez and Sebastian Ventura}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Smart Operators for Inducing Colorectal Cancer Classification Trees with PonyGE2 Grammatical Evolution Python Package}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Colorectal cancer is a disease that affects many people and requires a multidisciplinary approach, involving significant human and economic resources. We have been provided with a tabular dataset with 1.5 thousand cases of this disease. We are interested in producing interpretable classifiers for predicting the occurrence of complications. Grammatical Evolution has extensively been used for machine learning problems. In particular, it can be used to induce interpretable decision trees, with the advantage of allowing the practitioner to easily control the language by means of the grammar. PonyGE2 [1], [2] is a Python package that provides data scientists with Grammatical Evolution algorithms, which can be configured to their needs quite easily. In addition, and thanks to the benefits of the Python programming language, PonyGE2 is currently becoming more and more popular. However, the capabilities of PonyGE2 for inducing classification trees are still subject of improvement. In particular, it only uses simple equality conditions and requires to encode feature names and values with numbers. We have developed some smart operators for PonyGE2, which, not only enhance the framework in interpretability and performance when dealing with our colorectal cancer dataset, but also allows to produce results comparable to those of the widely known heuristic methods C4.5 and CART. We show how they could be applied to other datasets, and how they affect performance in our case.}, keywords = {Machine learning algorithms, Machine learning, Evolutionary computation, Germanium, Classification algorithms, Grammar, Task analysis, Grammatical Evolution, Classification Trees, Heterogeneous features, Colorectal Cancer}, doi = {doi:10.1109/CEC55065.2022.9870361}, notes = {Also known as \cite{9870361}}, ) @INPROCEEDINGS(Hohmann:2022:CEC, %xplor 24 Sep 2022 author = {Nikolas Hohmann and Mariusz Bujny and Jurgen Adamy and Markus Olhofer}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multi-objective 3D Path Planning for {UAVs} in Large-Scale Urban Scenarios}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In the context of real-world path planning applications for Unmanned Aerial Vehicles (UAVs), aspects such as handling of multiple objectives (e.g., minimizing risk, path length, travel time, energy consumption, or noise pollution), generation of smooth trajectories in 3D space, and the ability to deal with urban environments have to be taken into account jointly by an optimization algorithm to provide practically feasible solutions. Since the currently available methods do not allow for that, in this paper, we propose a holistic approach for solving a Multi-Objective Path Planning (MOPP) problem for UAVs in a three-dimensional, large-scale urban environment. For the tackled optimization problem, we propose an energy model and a noise model for a UAV, following a smooth 3D path. We utilize a path representation based on 3D Non-Uniform Rational B-Splines (NURBS). As optimizers, we use a conventional version of an Evolution Strategy (ES), two standard Multi-Objective Evolutionary Algorithms (MOEAs) - NSGA2 and MO-CMA-ES, and a gradient-based L-BFGS-B approach. To guide the optimization, we propose hybrid versions of the mentioned algorithms by applying an advanced initialization scheme that is based on the exact bidirectional Dijkstra algorithm. We compare the different algorithms with and without hybrid initialization in a statistical analysis, which considers the number of function evaluations and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 3D urban path planning scenario in New York City, based on real-world data exported from OpenStreetMap. The examination's results indicate that hybrid initialization is the main factor for the efficient identification of near-optimal solutions.}, keywords = {Solid modeling, Surface reconstruction, Three-dimensional displays, Urban areas, Evolutionary computation, Autonomous aerial vehicles, Trajectory, multi-objective optimization, three-dimensional, path planning, hybrid algorithms, evolutionary algorithms, UAV, unmanned aerial vehicle}, doi = {doi:10.1109/CEC55065.2022.9870265}, notes = {Also known as \cite{9870265}}, ) @INPROCEEDINGS(Sarafanov:2022:CEC, %xplor 24 Sep 2022 author = {Mikhail Sarafanov and Valerii Pokrovskii and Nikolay O. Nikitin}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolutionary Automated Machine Learning for Multi-Scale Decomposition and Forecasting of Sensor Time Series}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In the paper, we discuss the applicability of automated machine learning for the effective multi-scale modeling of the industrial sensors time series. The proposed approach is based on the evolutionary generative design of the composite modeling pipelines. The iterative data decomposition algorithm is proposed in the paper to improve the quality of the sensor time series forecasting. To effectively use it in an automated way, the boosting-like mutation operators have been implemented for graphs-based genotypes. The proposed approach reduced the forecast error by 1percent compared to the competitor library AutoTS. Also, the proposed modifications of the evolutionary algorithm resulted in better metrics in 7percent of the cases where they were used.}, keywords = {Machine learning algorithms, Time series analysis, Pipelines, Machine learning, Evolutionary computation, Predictive models, Prediction algorithms, time series, automated machine learning, multi-scale, sensors}, doi = {doi:10.1109/CEC55065.2022.9870347}, notes = {Also known as \cite{9870347}}, ) @INPROCEEDINGS(Pavlenko:2022:CEC, %xplor 24 Sep 2022 author = {Artem Pavlenko and Daniil Chivilikhin and Alexander Semenov}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Asynchronous Evolutionary Algorithm for Finding Backdoors in Boolean Satisfiability}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In this work we propose an asynchronous parallel evolutionary algorithm that is efficient for a specific type of gray-box optimization problems, in which the calculation of the fitness function may be split into a set of several independent calculations. An example of such an optimization problem is the search for backdoors (hidden structures) in the Boolean satisfiability problem: subsets of variables that allow an efficient splitting of the problem into a set of independent subproblems. Our experiments show that the proposed asynchronous approach allows speeding up the algorithm considerably, while also effi-ciently utilizing comnuting cluster time.}, keywords = {Computational modeling, Sociology, Clustering algorithms, Evolutionary computation, Search problems, Main-secondary, Task analysis, evolutionary algorithms, asynchronous algorithms, Boolean satisfiability problem, optimization}, doi = {doi:10.1109/CEC55065.2022.9870262}, notes = {Also known as \cite{9870262}}, ) @INPROCEEDINGS(Witten:2022:CEC, %xplor 24 Sep 2022 author = {Matthew Witten and Owen Clancey}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Vaccination Rates and the Emergence of Viral Variants: An Evolutionary Strategies-Inspired Model}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The emergence of variants of concern (VOC) has produced challenges in bringing the ongoing COVID-19 pandemic under control, and has created obstacles in the ultimate transition to endemicity; indeed, the omicron variant of SARS-CoV-2, the virus that causes COVID-19, caused a wave of unprecedented levels of infection throughout the globe. Although the available COVID-19 vaccines offer significant protection from infection, hospitalization, and death, challenges in vaccine availability globally have limited the efficacy of mass inoculation in controlling viral spread. The present study utilizes an evolutionary strategies (ES)-inspired model to examine the effect of vaccination upon the emergence of viral variants. The kinetics of the evolution of an idealized RNA virus are modelled in the details of the mutation of an ES population, and the emergence of variants is formulated as an optimization problem. A binary fitness function is optimized such that it achieves a maximum upon the condition that a single viral genome exhibits the requisite number of mutations such that it may be considered a variant of concern. Results demonstrate that vaccination is extremely effective in delaying the emergence of viral variants, and that vaccination rates are highly correlated with delayed variant emergence ($\mathrm{R}= 0.8399$) and requisite viral genetic diversity for consideration as a variant of concern ($\mathrm{R}=0.8583$). Time to emergence of a variant of concern for percent, 2percent, 5percent, and 8percent vaccination rates of the population are $17.46\pm 1.61$ generations, $20.51\pm 2.12$ generations, $33.67\pm 5.18$ generations, and $226.12\pm 114.41$ generations, respectively. It is shown that the delay in emergence of a variant of concern with higher vaccination rates may be attributable to a decrease in the number of infections, thus requiring a higher degree of genetic diversity in the viral genome.}, keywords = {COVID-19, Pandemics, RNA, Sociology, Genomics, Vaccines, Statistics, COVID-19, evolutionary strategies, vaccines viral variants}, doi = {doi:10.1109/CEC55065.2022.9870419}, notes = {Also known as \cite{9870419}}, ) @INPROCEEDINGS(Sargant:2022:CEC, %xplor 24 Sep 2022 author = {James Sargant and Sheridan Houghten and Michael Dube}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Evolving Weighted Contact Networks for Epidemic Modeling: the Ring and the Power}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={A generative evolutionary algorithm is used to evolve weighted personal contact networks that represent physical contact between individuals, and thus possible paths of infection during an epidemic. The evolutionary algorithm evolves a list of edge-editing operations applied to an initial graph. Two initial graphs are considered, a ring graph and a power-law graph. Different probabilities of infection and a wide range of weights are considered, which improve performance over other work. Modified edge operations are introduced, which also improve performance. It is shown that when trying to maximize epidemic duration, the best results are obtained when using the ring graph as the initial graph. When attempting to match a given epidemic profile, similar results are obtained when using either initial graph, but both improve performance over other work.}, keywords = {Epidemics, Computational modeling, Evolutionary computation}, doi = {doi:10.1109/CEC55065.2022.9870440}, notes = {Also known as \cite{9870440}}, ) @INPROCEEDINGS(Schweim:2022:CEC, %xplor 24 Sep 2022 author = {Dirk Schweim and Dominik Sobania and Franz Rothlauf}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Effects of the Training Set Size: A Comparison of Standard and Down-Sampled Lexicase Selection in Program Synthesis}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={From a practitioners perspective, the number of in-put/output examples used during the training process in program synthesis studies is too large, as in practice, these examples must be labeled by hand. Therefore, this paper analyzes the influence of different training set sizes on the performance, generalization ability, as well as the structure of the programs generated by grammar-guided genetic programming. We compare down-sampled lexicase selection with standard lexicase selection on three common problems from the general program synthesis benchmark suite. First, we find that both lexicase variants are robust against reducing the amount of training data. We find that standard lexicase has a tendency to overfit the training data on some problems. With down-sampled lexicase, in contrast, overfitting on training data is reduced and evolved programs generalize better on held-out test cases. Consequently, we suggest to use grammar-guided genetic programming with down-sampled lexicase selection in the program synthesis domain.}, keywords = {Training, Training data, Genetic programming, Evolutionary computation, Benchmark testing, Programming, Standards, Program synthesis, automatic programming, evolutionary algorithms, genetic programming}, doi = {doi:10.1109/CEC55065.2022.9870337}, notes = {Also known as \cite{9870337}}, ) @INPROCEEDINGS(Mauri:2022:CEC, %xplor 24 Sep 2022 author = {Geraldo R. Mauri and Luiz H. N. Lorena and Luiz A. N. Lorena and Antonio A. Chaves}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Solving the Point Feature Cartographic Label Placement problem using Jaccard index as a measure of labels intersection}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper presents a new approach to solve the Point Feature Cartographic Label Placement (PFCLP) problem. The PFCLP is a relevant problem for Geographic Information Systems, where the objective is to position labels on a map avoiding overlaps to improve legibility. The first techniques proposed to solve this problem considered only the presence/absence of over-lapping, while more recent ones introduced the idea of estimating the intensity of such overlap. This work extends previous works by considering the overlap intensity through the Jaccard index. Traditional mathematical models for PFCLP were compared to the proposed strategy showing that the Jaccard index provided the best results in terms of legibility. A Clustering Search (CS) algorithm was also proposed to compare the performance of our strategy on a set of large-sized instances. The experimental results show good solutions for instances with up to 13206 points.}, keywords = {Adaptation models, Computational modeling, Scattering, Estimation, Clustering algorithms, Euclidean distance, Evolutionary computation, Cartographic Label Placement, Jaccard index, Clustering Search, Local Branching}, doi = {doi:10.1109/CEC55065.2022.9870276}, notes = {Also known as \cite{9870276}}, ) @INPROCEEDINGS(Lorena:2022:CEC, %xplor 24 Sep 2022 author = {Luiz H. N. Lorena and Antonio A. Chaves and Geraldo R. Mauri and Luiz A. N. Lorena}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An Adaptive Biased Random-key Genetic Algorithm for Rank Aggregation with Ties and Incomplete Rankings}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={The Rank Aggregation problem has applications in several fields of science like Information Retrieval, Social Sciences, Bioinformatics, among others. In this problem, the objective is to create a consensus ranking given a set of input rankings. This paper considers a variant of this problem, in which the set of input rankings may contain ties and may be incomplete. An Adaptive Biased Random-key Genetic Algorithm (A-BRKGA) is proposed to solve this problem, and the results are compared to an integer linear programming model previously introduced in the literature. Since the fitness evaluation of the A-BRKGA is its most time-consuming component, a partial fitness evaluation was derived and used in the local search component created for this problem. The partial evaluation reduces the time complexity from quadratic to linear. In the experiments, the efficiency of the A-BRKGA with the local search was evaluated against the A-BRKGA without the local search component and the integer linear programming introduced in the literature. The experimental results show that the proposed technique achieved superior results in terms of quality when compared to the A-BRKGA without the local search component, achieved similar results in terms of quality and better results in terms of computational time when compared to the integer linear programming model.}, keywords = {Adaptation models, Computational modeling, Vehicle routing, Social sciences, Metaheuristics, Integer linear programming, Search problems, Metaheuristic, Generalized Kemeny-aggregation problem, Rank aggregation with ties, Consensus ranking problem}, doi = {doi:10.1109/CEC55065.2022.9870203}, notes = {Also known as \cite{9870203}}, ) @INPROCEEDINGS(Molina-Perez:2022:CEC, %xplor 24 Sep 2022 author = {Daniel {Molina Perez} and Edgar {Alfredo Portilla-Flores} and Efren Mezura-Montes and Eduardo Vega-Alvarado}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An improved Estimation of Distribution Algorithm for Solving Constrained Mixed-Integer Nonlinear Programming Problems}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In a mixed-integer nonlinear programming problem, integer restrictions divide the feasible region into discontinuous feasible parts with different sizes. Evolutionary Algorithms (EAs) are usually vulnerable to being trapped in larger discontinuous feasible parts. In this work, an improved version of an Estimation of Distribution Algorithm (EDA) is developed, where two new op-erations are proposed. The first one establishes a link between the learning-based histogram model and the $\varepsilon$ -constrained method. Here, the constraint violation level of the $\varepsilon$ -constrained method is used to explore the smaller discontinuous parts and form a better statistical model. The second operation is the hybridization of the EDA with a mutation operator to generate offspring from both the global distribution information and the parent information. A benchmark is used to test the performance of the improved proposal. The results indicated that the proposed approach shows a better performance against other tested EAs. This new proposal solves to a great extent the influence of the larger discontinuous feasible parts, and improve the local refinement of the real variables.}, keywords = {Histograms, Estimation, Evolutionary computation, Programming, Benchmark testing, Proposals, estimation of distribution algorithm, evolution-ary algorithms, integer restriction handling, mixed integer non-linear programming}, doi = {doi:10.1109/CEC55065.2022.9870338}, notes = {Also known as \cite{9870338}}, ) @INPROCEEDINGS(Morais:2022:CEC, %xplor 24 Sep 2022 author = {Igor Morais and Gabriel Souto and Glaydston Mattos Ribeiro and Israel Mendonca and Pedro Henrique Gonzalez}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={A Hybrid {BRKGA} Approach for the Multiproduct Two Stage Capacitated Facility Location Problem}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={This paper presents a hybrid BRKGA (MP-HBRKGA), that combines BRKGA with a Local Branching technique, to solve the multiproduct two-stage capacitated facility location problem (MP-TSCFLP). In this problem, a set of different products has to be transported from a set of factories, passing through a set of depots (first stage) and then transported to a set of customers (second stage). The goal in the MP-TSCFLP is to minimize the opening and transportation costs, where each kind of product has its own transportation cost per unit transported. Recent hybrid methods have been successfully applied to facility location problems, therefore, in this paper we propose adaptations of such hybrid methods and implement the MP-HBRKGA for handling the multiproduct characteristic. To the best of our knowledge, such hybrid BRKGA presented the best results for the single-product problem and have not yet been applied to solve the problem with multiple products. Computational experiments compare the obtained results to those in the literature, using four sets, with different characteristics, of large-sized instances, proposed in the literature.}, keywords = {Costs, Transportation, Evolutionary computation, Production facilities, Multiproduct Two-Stage Capacitated Facility Location, BRKGA, Local Branching, Matheuristic, Supply Chain}, doi = {doi:10.1109/CEC55065.2022.9870321}, notes = {Also known as \cite{9870321}}, ) @INPROCEEDINGS(Da-Silva:2022:CEC, %xplor 24 Sep 2022 author = {Andre Ramos Fernandes {Da Silva} and Lucas Marcondes Pavelski and Luiz Alberto Queiroz {Cordovil Junior} and Paulo Henrique {De Oliveira Gomes} and Layane Menezes Azevedo and Francisco Erivaldo {Fernandes Junior}}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={An evolutionary search algorithm for efficient {ResNet-based} architectures: a case study on gender recognition}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Neural Architecture Search (NAS) is a busy research field growing exponentially in recent years. State-of-the-art deep neural networks usually require a specialist to fine-tune the model to solve a specific problem. NAS research aims to design neural network architectures automatically, thus easing the need for machine learning specialists to spend a lot of effort on hand-crafted attempts. As artificial intelligence applications are becoming ubiquitous, there is also a growing interest in efficient applications that could be deployed to smartphones, smart wearable devices, and other edge devices. Gender recognition in unfiltered images -- such as those we find in real-world situations like pictures taken with smartphones and video shots from surveillance cameras -- is one of such challenging applications. In this work, we developed an evolutionary NAS algorithm that consistently finds efficient ResNet-based architectures, named RENNAS, which have a good trade-off between classification accuracy and architectural and computational complexities. We demonstrate our algorithm's performance on Adience dataset of unfiltered images for gender recognition.}, keywords = {Image coding, Wearable computers, Surveillance, Image edge detection, Neural networks, Computer architecture, Classification algorithms, Evolutionary Neural Architecture Search, Residual Neural Networks, Genetic Algorithms, Computer Vision, Gender Recognition}, doi = {doi:10.1109/CEC55065.2022.9870434}, notes = {Also known as \cite{9870434}}, ) @INPROCEEDINGS(Sun:2022:CEC, %xplor 24 Sep 2022 author = {Bo Sun and Wei Li and Ying Huang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Performance of Composite {PPSO} on Single Objective Bound Constrained Numerical Optimization Problems of CEC 2022}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Particle swarm optimization has been extensively noticed for its fast convergence speed with few parameters. However, it would be plagued by the premature convergence only affected by the global particles. In this study, Composite Proactive Particles in Swarm Optimization (Co-PPSO) is proposed. In Co-PPSO, the composite strategy framework is embedded into Proactive Particles in Swarm Optimization (PPSO), that is, three learning strategies are proposed to evaluate their differences and select the most suitable one for each particle. In addition, an elite group is constructed to make the particles jump out of the situation that they are only affected by the global best one in the particle swarm, and further improve the convergence accuracy. CEC2022 competition of single objective bound-constrained numerical optimization is employed to test the effect of 10-$D$ and 20-$D$ optimization, and four well-known PSO variants were used for comparison. The experimental results show that the Co-PPSO has certain competitiveness to improve premature convergence.}, keywords = {Sociology, Particle swarm optimization, Time complexity, Statistics, Optimization, Convergence, particle swarm optimization, composite strategy framework, elite group, CEC2022}, doi = {doi:10.1109/CEC55065.2022.9870369}, notes = {Also known as \cite{9870369}}, ) @INPROCEEDINGS(Ji:2022:CEC, %xplor 24 Sep 2022 author = {Hebing Ji and Shaojie Chen and Qinqin Fan}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Zoning Search and Transfer Learning-based Multimodal Multi-objective Evolutionary Algorithm}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Multimodal multi-objective optimization (MMO) not only finds a good Pareto front (PF) approximation in the objective space, but also locates sufficient equivalent Pareto optimal solutions in the decision space. Although the zoning search (ZS) can improve the population diversity and reduce the problem complexity, it searches each subspace independently. This may waste computational resources. To alleviate the above issue, a zoning search and transfer learning- based multimodal multi-objective evolutionary algorithm (called ZSTL-MMOEA) is proposed in the present study. In the ZSTL-MMOEA, the decision space is divided into many subspaces and the transfer learning is used to realize knowledge sharing between two the most similar subspaces. The ZSTL-MMOEA is compared with five recently proposed multimodal multi-objective evolutionary algorithms (MMOE- As) on 22 test functions. Experimental results show that the proposed algorithm outperforms its competitors in most functions.}, keywords = {Sensitivity, Transfer learning, Sociology, Evolutionary computation, Pareto optimization, Search problems, Complexity theory, Multimodal multi-objective optimization, evolutionary computation, zoning search, transfer learning}, doi = {doi:10.1109/CEC55065.2022.9870346}, notes = {Also known as \cite{9870346}}, ) @INPROCEEDINGS(Sun:2022:CEC, %xplor 24 Sep 2022 author = {Bo Sun and Yafeng Sun and Wei Li}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Multiple Topology {SHADE} with Tolerance-based Composite Framework for CEC2022 Single Objective Bound Constrained Numerical Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={To further enhance the convergence performance and accuracy of SHADE, a SHADE with tolerance-based multiple topology selection framework (MTT_SHADE) is proposed in this paper. In MTT_SHADE, three population topologies are established employing the k-nearest neighbor network, small-world network, and random network, respectively, and the evolution of individuals depends on the neighborhoods derived from different topologies. The tolerance-based composite framework is proposed to select the appropriate topology for an individual at the same time. Specifically, local tolerance and global tolerance are predetermined, corresponding to the tolerance for individuals and the population, respectively. The topology involved in the evolution of the individual is replaced when the individual does not progress after successive iterations. The population that does not improve in effect after successive iterations are considered to have exceeded the global tolerance and the three population topologies are reconstructed. The CEC2022 competition on single objective bound-constrained numerical optimization and four state-of-the-art DE variants are employed to investigate the effectiveness of the proposed algorithm. Experimental results show that MTT_SHADE is competitive in terms of accuracy and convergence.}, keywords = {Network topology, Sociology, Evolutionary computation, Topology, Statistics, Time complexity, Optimization, Differential evolution algorithm, k-nearest neighbor, small-world, random network, CEC2022}, doi = {doi:10.1109/CEC55065.2022.9870395}, notes = {Also known as \cite{9870395}}, ) @INPROCEEDINGS(Xia:2022:CEC, %xplor 24 Sep 2022 author = {Hai Xia and Changhe Li and Sanyou Zeng and Qingshan Tan and Junchen Wang and Shengxiang Yang}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Learning to Search Promising Regions by a Monte-Carlo Tree Model}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={In complex optimization problems, learning where to search is a difficult but critical decision for all search algorithms. Evolutionary computation methods also encounter a dilemma about where to explore or exploit. In this paper, a Monte-Carlo tree is constructed to guide evolutionary algorithms to search multiple promising regions simultaneously. In the Monte-Carlo tree model, a root node that contains all historical solutions represents the whole solution space. In each node of the tree, with k-means clustering method to partition solutions into different groups, group labels of the solutions are used to train support vector regression, which can learn a boundary to partition a region into different sub-regions. According to state values of nodes, reproduction operators of evolutionary algorithms are strengthened by selecting solutions in the most promising regions. From experimental results on multimodal problems, the proposed algorithm shows a competitive performance, which also indicates a great potential for applications to other kinds of optimization problems.}, keywords = {Support vector machines, Constraint optimization, Monte Carlo methods, Computational modeling, Static VAr compensators, Evolutionary computation, Machine learning, Evolutionary computation, Monte-Carlo tree, promising region, space partitioning}, doi = {doi:10.1109/CEC55065.2022.9870281}, notes = {Also known as \cite{9870281}}, ) @INPROCEEDINGS(Bajaj:2022:CEC, %xplor 24 Sep 2022 author = {Anu Bajaj and Ajith Abraham}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Test Case Prioritization and Reduction Using Hybrid Quantum-behaved Particle Swarm Optimization}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={Regression testing is an integral part of the software evolution and maintenance phase as it ensures that the modified software is working correctly after any upgrades. Test case prioritization and reduction minimize cost and effort needed for retesting by scheduling critical test cases before the less critical ones and removing redundant test cases. The criticality and redundancy of the test cases depend on several testing criteria. This paper empirically analyzed the effect of different testing criteria like code and fault coverage on the techniques' performance. This paper proposed a discrete Quantum-behaved particle swarm optimization (QPSO) for enhancing efficiency of test case prioritization. The algorithm is improved by replacing the random distribution with Gaussian probability to escape from the local optima. The evolution stagnation issue is further resolved by hybridizing it with genetic algorithm (QPSO-GA). In addition to prioritizing the test cases, the algorithm also reduces the test suite size through the test suite reduction approach. The experiments are conducted on different versions of three pro-grams from the open-source software infrastructure repository. The performance is compared with the average percentage of statement coverage, fault detection, and their combinations with the cost. Consequently, suite reduction, fault detection capability losses, and coverage loss percentage are also drawn for test suite reduction. The proposed algorithms outperformed the random search, ant colony optimization, differential evolution, GA, PSO, and adaptive PSO for all the evaluation metrics.}, keywords = {Costs, Fault detection, Software algorithms, Redundancy, Software quality, Proposals, Particle swarm optimization, regression testing, nature-inspired algorithms, test case prioritization, test suite reduction, particle swarm optimization, QPSO}, doi = {doi:10.1109/CEC55065.2022.9870238}, notes = {Also known as \cite{9870238}}, ) @INPROCEEDINGS(:2022:CEC, %xplor 24 Sep 2022 author = {}, booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, title={Author Index}, year={2022}, editor = {Carlos A. Coello Coello and Sanaz Mostaghim}, address = {Padua, Italy}, month = {18-23 July}, ISBN13 = {978-1-6654-6708-7}, abstract={}, keywords = {}, doi = {doi:10.1109/CEC55065.2022.9870214}, notes = {Also known as \cite{9870214}}, )