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Joint filter and channel pruning of convolutional neural networks as a bi-level optimization problem Memetic Comp. (IF 4.7) Pub Date : 2024-02-17 Hassen Louati, Ali Louati, Slim Bechikh, Elham Kariri
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Stock portfolio optimization based on factor analysis and second-order memetic differential evolution algorithm Memetic Comp. (IF 4.7) Pub Date : 2024-02-10 Ning Han, Yinnan Chen, Lingjuan Ye, Xinchao Zhao
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Expensive many-objective evolutionary optimization guided by two individual infill criteria Memetic Comp. (IF 4.7) Pub Date : 2023-12-19 Shufen Qin, Chaoli Sun, Farooq Akhtar, Gang Xie
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Emotion-aware brain storm optimization Memetic Comp. (IF 4.7) Pub Date : 2023-11-27 Charis Ntakolia, Dimitra-Christina C. Koutsiou, Dimitris K. Iakovidis
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Top-level dual exploitation particle swarm optimization Memetic Comp. (IF 4.7) Pub Date : 2023-11-20 Chan Huang, Jinhao Yu, Junhui Yang
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An optimization method for pruning rates of each layer in CNN based on the GA-SMSM Memetic Comp. (IF 4.7) Pub Date : 2023-11-17 Xiaoyu Dong, Pinshuai Yan, Mengfei Wang, Binqi Li, Yuantao Song
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A decomposition-based many-objective evolutionary algorithm with weight grouping and adaptive adjustment Memetic Comp. (IF 4.7) Pub Date : 2023-11-13 Xiaoxin Gao, Fazhi He, Jinkun Luo, Tongzhen Si
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Multimodal multi-objective optimization with multi-stage-based evolutionary algorithm Memetic Comp. (IF 4.7) Pub Date : 2023-09-26 Tianyong Wu, Fei Ming, Hao Zhang, Qiying Yang, Wenyin Gong
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A point crowding-degree based evolutionary algorithm for many-objective optimization Memetic Comp. (IF 4.7) Pub Date : 2023-09-08 Cai Dai, Cheng Peng, Xiujuan Lei
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A tolerance-based memetic algorithm for constrained covering array generation Memetic Comp. (IF 4.7) Pub Date : 2023-08-29 Xu Guo, Xiaoyu Song, Jian-tao Zhou, Feiyu Wang
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Learning to estimate optical flow using dual-frequency paradigm Memetic Comp. (IF 4.7) Pub Date : 2023-08-28 Yujin Zheng, Chu He, Yan Huang, Shenghua Fan, Min Jiang, Dingwen Wang, Yang Yi
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Multiple population-based multi-objective evolutionary semi-supervised multi-kernel region fuzzy clustering image segmentation Memetic Comp. (IF 4.7) Pub Date : 2023-08-26 Feng Zhao, Mimi Zhang, Hanqiang Liu
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Dynamic matrix-based evolutionary algorithm for large-scale sparse multiobjective optimization problems Memetic Comp. (IF 4.7) Pub Date : 2023-08-22 Feiyue Qiu, Huizhen Hu, Jin Ren, Liping Wang, Xiaotian Pan, Qicang Qiu
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A biologically inspired approach for recovering the trajectory of offline handwriting Memetic Comp. (IF 4.7) Pub Date : 2023-08-16 Rosa Senatore, Adolfo Santoro, Antonio Parziale, Angelo Marcelli
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Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio Memetic Comp. (IF 4.7) Pub Date : 2023-08-09 Zhengping Liang, Canran Chen, Xiyu Wang, Ling Liu, Zexuan Zhu
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A knowledge learning and random pruning-based memetic algorithm for user route planning in bike-sharing system Memetic Comp. (IF 4.7) Pub Date : 2023-06-14 Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
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Event-driven spiking neural networks with spike-based learning Memetic Comp. (IF 4.7) Pub Date : 2023-05-18 Limiao Ning, Junfei Dong, Rong Xiao, Kay Chen Tan, Huajin Tang
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A motivational model based on artificial biological functions for the intelligent decision-making of social robots Memetic Comp. (IF 4.7) Pub Date : 2023-05-13 Marcos Maroto-Gómez, María Malfaz, Álvaro Castro-González, Miguel Ángel Salichs
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A regularity model-based multi-objective estimation of distribution memetic algorithm with auto-controllable population diversity Memetic Comp. (IF 4.7) Pub Date : 2023-02-23 Qiaoyong Jiang, Jianan Cui, Lei Wang, Yanyan Lin, Yali Wu, Xinhong Hei
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Inferring sparse genetic regulatory networks based on maximum-entropy probability model and multi-objective memetic algorithm Memetic Comp. (IF 4.7) Pub Date : 2022-12-28 Fu Yin, Jiarui Zhou, Weixin Xie, Zexuan Zhu
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Efficient automatically evolving convolutional neural network for image denoising Memetic Comp. (IF 4.7) Pub Date : 2022-12-08 Fang Wei, Zhu Zhenhao, Hong Zhou, Zhang Tao, Sun Jun, Wu Xiaojun
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A single-solution–compact hybrid algorithm for continuous optimization Memetic Comp. (IF 4.7) Pub Date : 2022-12-03 Souheila Khalfi, Giovanni Iacca, Amer Draa
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Fuzzy logic based multi-objective optimization of a multi-agent transit control system Memetic Comp. (IF 4.7) Pub Date : 2022-12-01 Nabil Morri, Sameh Hadouaj, Lamjed Ben Said
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A bi-level transformation based evolutionary algorithm framework for equality constrained optimization Memetic Comp. (IF 4.7) Pub Date : 2022-11-01 Lei Chen, Haosen Liu, Hai-Lin Liu, Fangqing Gu
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Optimal operator selection based on the hybrid operator selection strategy Memetic Comp. (IF 4.7) Pub Date : 2022-10-22 Hongling Chen, Yanyan Tan, Zeyuan Yan, Lili Meng, Wenbo Wan
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Multi-objective approaches to portfolio optimization with market impact costs Memetic Comp. (IF 4.7) Pub Date : 2022-10-22 Hongze Wang, Xuerong Li, Wenjing Hong, Ke Tang
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Offline data-driven optimization based on dual-scale surrogate ensemble Memetic Comp. (IF 4.7) Pub Date : 2022-10-16 Junhua Ku, Huixiang Zhen, Wenyin Gong
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A diversity-aware memetic algorithm for the linear ordering Problem Memetic Comp. (IF 4.7) Pub Date : 2022-10-16 Lázaro Lugo, Carlos Segura, Gara Miranda
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A hybrid heuristic approach with adaptive scalarization for linear semivectorial bilevel programming and its application Memetic Comp. (IF 4.7) Pub Date : 2022-10-15 Hong Li, Li Zhang
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A novelty-search-based evolutionary reinforcement learning algorithm for continuous optimization problems Memetic Comp. (IF 4.7) Pub Date : 2022-10-15 Chengyu Hu, Rui Qiao, Wenyin Gong, Xuesong Yan, Ling Wang
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HAS-EA: a fast parallel surrogate-assisted evolutionary algorithm Memetic Comp. (IF 4.7) Pub Date : 2022-10-11 Yixian Li, Jinghui Zhong
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Dual transfer learning with generative filtering model for multiobjective multitasking optimization Memetic Comp. (IF 4.7) Pub Date : 2022-07-26 Qianlong Dang, Weifeng Gao, Maoguo Gong
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Optimization of integrated production scheduling and vehicle routing problem with batch delivery to multiple customers in supply chain Memetic Comp. (IF 4.7) Pub Date : 2022-07-23 Tanzila Azad, Humyun Fuad Rahman, Ripon K. Chakrabortty, Michael J. Ryan
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A mathematical analysis of EDAs with distance-based exponential models Memetic Comp. (IF 4.7) Pub Date : 2022-07-16 Imanol Unanue, María Merino, Jose A. Lozano
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Multiresolution community detection in complex networks by using a decomposition based multiobjective memetic algorithm Memetic Comp. (IF 4.7) Pub Date : 2022-07-11 Zengyang Shao, Lijia Ma, Yuan Bai, Shanfeng Wang, Qiuzhen Lin, Jianqiang Li
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A hierarchical taxonomic survey of spiking neural networks Memetic Comp. (IF 4.7) Pub Date : 2022-07-09 Siqi Wang, Tee Hiang Cheng, Meng Hiot Lim
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A multiobjective memetic algorithm for integrated process planning and scheduling problem in distributed heterogeneous manufacturing systems Memetic Comp. (IF 4.7) Pub Date : 2022-05-09 Qihao Liu, Xinyu Li, Liang Gao, Guangchen Wang
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Multi-objective deep reinforcement learning for emergency scheduling in a water distribution network Memetic Comp. (IF 4.7) Pub Date : 2022-05-06 Chengyu Hu, Qiuming Wang, Wenyin Gong, Xuesong Yan
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Adaptive multiobjective evolutionary algorithm for large-scale transformer ratio error estimation Memetic Comp. (IF 4.7) Pub Date : 2022-05-04 Changwu Huang, Lianghao Li, Cheng He, Ran Cheng, Xin Yao
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A generic method to compose an algorithm portfolio with a problem set of unknown distribution Memetic Comp. (IF 4.7) Pub Date : 2022-05-03 Wenwen Liu, Shiu Yin Yuen, Chi Wan Sung
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Metaheuristic approaches for ratio cut and normalized cut graph partitioning Memetic Comp. (IF 4.7) Pub Date : 2022-04-29 Gintaras Palubeckis
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Thematic issue on knowledge and data driven evolutionary multi-objective optimization Memetic Comp. (IF 4.7) Pub Date : 2022-04-19 Ran Cheng,Jinliang Ding,Wenli Du,Yaochu Jin
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Constrained Multi-Objective Optimization with a Limited Budget of Function Evaluations Memetic Comp. (IF 4.7) Pub Date : 2022-04-08 Roy de Winter, Philip Bronkhorst, Bas van Stein, Thomas Bäck
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Handling constrained multi-objective optimization problems with heterogeneous evaluation times: proof-of-principle results Memetic Comp. (IF 4.7) Pub Date : 2022-03-07 Julian Blank, Kalyanmoy Deb
Most real-world optimization problems consist of multiple objectives to be optimized and multiple constraints to be satisfied. Moreover, the performance assessment of the objective and constraints often requires running different software packages separately along with evaluating mathematically defined functions with significantly different (heterogeneous) computing times. A single software package
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CCMBO: a covariance-based clustered monarch butterfly algorithm for optimization problems Memetic Comp. (IF 4.7) Pub Date : 2022-03-05 Samaneh Yazdani, Esmaeil Hadavandi, Mohammad Mirzaei
Rotationally variance nature-inspired algorithms are not efficient for solving non-separable problems. One way for solving this limitation is utilizing the concept of covariance-based learning to transform the original space into the new space in which the interactions among variables are revealed and operators perform in an appropriate coordinate system. In this paper, Monarch butterfly optimization
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Preference based multi-objective reinforcement learning for multi-microgrid system optimization problem in smart grid Memetic Comp. (IF 4.7) Pub Date : 2022-02-22 Jiangjiao Xu, Ke Li, Mohammad Abusara
Grid-connected microgrids comprising renewable energy, energy storage systems and local load, play a vital role in decreasing the energy consumption of fossil diesel and greenhouse gas emissions. A distribution power network connecting several microgrids can promote more potent and reliable operations to enhance the security and privacy of the power system. However, the operation control for a multi-microgrid
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The design of evolutionary feature selection operator for the micro-expression recognition Memetic Comp. (IF 4.7) Pub Date : 2022-02-18 Zhan WangPing, Jiang Min, Yao JunFeng, Liu KunHong, Wu QingQiang
The evolutionary algorithm is widely deployed in the feature selection task, but the complexity of the solution for the feature selection problem grows exponentially with the increase of feature dimensions. To handle this problem, this study proposes a novel feature selection operator for the Micro-Expression (ME) recognition task based on Genetic Programming, which can reduce the complexity caused
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A constrained multi-objective optimization algorithm with two cooperative populations Memetic Comp. (IF 4.7) Pub Date : 2022-02-08 Jianlin Zhang, Jie Cao, Fuqing Zhao, Zuohan Chen
Constrained multi-objective problems (CMOPs) require balancing convergence, diversity, and feasibility of solutions. Unfortunately, the existing constrained multi-objective optimization algorithms (CMOEAs) exhibit poor performance when solving the CMOPs with complex feasible regions. To solve this shortcoming, this work proposes an improved algorithm named the CMOEA-TCP, which maintains two populations
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Solving large-scale multiobjective optimization via the probabilistic prediction model Memetic Comp. (IF 4.7) Pub Date : 2022-01-31 Haokai Hong, Kai Ye, Min Jiang, Donglin Cao, Kay Chen Tan
The characteristic of large-scale multiobjective optimization problems (LSMOPs) is optimizing multiple conflicting objectives while considering thousands of decision variables at the same time. An efficient optimization algorithm for LSMOPs should have the ability to search a large decision space and find the global optimum in the objective space. Maintaining the diversity of the population is one
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A particle swarm optimization based multiobjective memetic algorithm for high-dimensional feature selection Memetic Comp. (IF 4.7) Pub Date : 2022-01-29 Juanjuan Luo, Dongqing Zhou, Lingling Jiang, Huadong Ma
Feature selection, as one of the dimension reduction methods, is a crucial processing step in dealing with high-dimensional data. It tries to preserve feature subset representing the whole feature space, which aims to reduce redundancy and increase the classification accuracy. Since the two objectives are usually in conflict with each other, feature selection is modeled as a multi-objective problem
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Multi-objective LSTM ensemble model for household short-term load forecasting Memetic Comp. (IF 4.7) Pub Date : 2022-01-25 Fan, Chaodong, Li, Yunfan, Yi, Lingzhi, Xiao, Leyi, Qu, Xilong, Ai, Zhaoyang
With the development of smart grid, household load forecasting played an important role in power system operations. However, the household load forecasting is often inefficient due to its high volatility and uncertainty. Consequently, a multi-objective LSTM ensemble model based on the DBN combination strategy, is proposed in this paper. This method first builds a deep learning framework based on the
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A novel multimodal multiobjective memetic algorithm with a local detection mechanism and a clustering-based selection strategy Memetic Comp. (IF 4.7) Pub Date : 2022-01-21 Luo, Naili, Ye, Yulong, Lin, Wu, Lin, Qiuzhen, Leung, Victor C. M.
In real-world scenes involving multimodal multiobjective optimization, there may exist different Pareto optimal sets (PSs) for the same Pareto front (PF), and some PFs even need to reserve all PSs, including local and global PSs. Most existing multimodal multiobjective optimization algorithms often distinguish solutions according to their diversity and convergence performances in the objective space
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Solving vehicle routing problem by memetic search with evolutionary multitasking Memetic Comp. (IF 4.7) Pub Date : 2022-01-18 Shang, Qingxia, Huang, Yuxiao, Wang, Yu, Li, Min, Feng, Liang
Vehicle routing problem (VRP) is a well-known NP-hard combinational optimization problem. In the literature, existing approaches can be generally classified into two categories: exact methods and metaheuristics methods. The former is only effective for small problem instances while the latter is more suitable for practical applications with larger scale. However, these methods perform evolutionary
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A framework for expensive many-objective optimization with Pareto-based bi-indicator infill sampling criterion Memetic Comp. (IF 4.7) Pub Date : 2021-11-27 Song, Zhenshou, Wang, Handing, Xu, Hongbin
Surrogate-assisted many-objective optimization is to locate Pareto optimal solutions using a limited number of function evaluations. Most existing surrogate-assisted evolutionary algorithms are designed to embed in a specific many-objective evolutionary algorithm. The Pareto-based bi-indicator infill sampling criterion has been proven to be effective in saving expensive evaluations in surrogate-assisted
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A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems Memetic Comp. (IF 4.7) Pub Date : 2021-11-13 Liu, Tianyu, Cao, Lei, Wang, Zhu
Dynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This paper presents a dynamic multiobjective evolutionary framework (DMOEF-MS), which adopts a novel multipopulation structure and Steffensen’s method to solve DMOPs. In DMOEF-MS, only one population deals with the original DMOP, while the others focus
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Dynamic grid-based uniform search for solving constrained multiobjective optimization problems Memetic Comp. (IF 4.7) Pub Date : 2021-11-13 Yuan, Jiawei
When solving constrained multiobjective optimization problems (CMOPs), it is important to uniformly explore the promising regions that are not dominated by feasible solutions, and this can effectively avoid the loss of the Pareto front fragments. To achieve this, we propose a grid-based uniform search (GUS) to guide the current population to search the promising areas uniformly in this paper. Therein
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Parameter adaptation in multifactorial evolutionary algorithm for many-task optimization Memetic Comp. (IF 4.7) Pub Date : 2021-10-05 Thang, Ta Bao, Dao, Tran Cong, Long, Nguyen Hoang, Binh, Huynh Thi Thanh
The advent of multifactorial optimization (MFO) has made a wind of change in intelligence computation in general and specifically in evolutionary computing. Based on the implicit parallelism of population-based search, MFO optimizes different problems simultaneously and entirely. However, the randomness of knowledge transfers raises the question of how to diminish harmful interactions among tasks for
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System-in-package design using multi-task memetic learning and optimization Memetic Comp. (IF 4.7) Pub Date : 2021-09-23 Dai, Weijing, Wang, Zhenkun, Xue, Ke
System-in-Package (SiP) is an advanced packaging technology and developing rapidly in semiconductor industry. Electronic modules of this package type are individual integrated systems for specific applications. Therefore, those modules are usually characterized by multiple encapsulated components and sophisticated internal structures. However, such complexity brings great challenges to package design
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Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model Memetic Comp. (IF 4.7) Pub Date : 2021-09-18 Rabhi, Besma, Elbaati, Abdelkarim, Boubaker, Houcine, Hamdi, Yahia, Hussain, Amir, Alimi, Adel M.
Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework