[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84597-en":3,"doc-seo-84597-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84597,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","From Consistency to Collaborative Discovery: MFEA-CoD for Multitask Novelty Search","Evolutionary multitasking (EMT) can solve multiple optimization problems together by leveraging latent inter-task consistency, which often accelerates convergence to predefined optima. This work shifts EMT from consistency-based acceleration to collaborative discovery by proposing MFEA-CoD, a multifactorial evolutionary algorithm for multitask novelty search. It coordinates novelty-search tasks using a multitask repulsion operator to reduce redundant behavioral discoveries and an adaptive inter-task transfer mechanism to reuse discovery opportunities. The method further extends to novelty-augmented optimization to mitigate deceptive-objective premature convergence, validated across multiple benchmark problem types.","From Consistency to Collaborative Discovery: MFEA-CoD for Multitask Novelty Search  \nJiao Liu, Yanchi Li, Hua Yu, Abhishek Gupta, and Yew-Soon Ong  \narXiv :2607 .0076 1v2 [ cs .NE] 2 Jul 2026  \nAbstract—Evolutionary multitasking (EMT) has shown strong capability in solving multiple optimization problems simultaneously by exploiting latent inter-task consistency, such as similarities in promising solutions or search directions. However, most existing EMT studies remain focused on objective-driven optimization, where such consistency is mainly used to accelerate convergence toward predefined optima. In this paper, we move EMT from consistency to collaborative discovery and propose amultifactorial evolutionary algorithm with collaborative discovery (MFEA-CoD) for multitask novelty search. Unlike conventional EMT, MFEA-CoD coordinates multiple novelty search tasks to collaboratively discover behaviorally novel solutions rather than merely transferring consistent search information for faster convergence. Specifically, a multitask repulsion operator encourages different tasks to explore distinct regions of the unified search space, thereby reducing redundant behavioral discoveries. Meanwhile, an adaptive inter-task transfer mechanism exploits shared discovery opportunities in overlapping novelty-improving regions by adjusting the transfer probability according to the online contribution of transferred information. Furthermore, MFEACoD is extended to multitask novelty-augmented optimization, where behavioral novelty is jointly considered with objective information to alleviate premature convergence caused by deceptive objectives. Experiments on synthetic basin-type problems, deceptive maze navigation problems, MuJoCo policy optimization problems, and generative novelty search problems demonstrate that MFEA-CoD improves the efficiency of discovering diverse novel solutions and shows clear advantages in deceptive objective landscapes.  \nIndex Terms—Evolutionary multitasking, novelty search, multifactorial evolutionary algorithm, collaborative discovery  \nI. INTRODUCTION  \nEvolutionary multitasking (EMT) has recently emerged as a promising optimization paradigm in the evolutionary computation community [1], [2] . Unlike conventional approaches that address optimization problems independently, EMT enables the simultaneous solving of multiple related optimization tasks within a unified framework [3] . By exploiting latent inter-task relationships during evolution, EMT enables the transfer of consistent search information across tasks, such as solution similarity [4] and search-direction consistency [5], thereby  \nThe authors acknowledge the use of Gemini and ChatGPT to identify improvements in the writing.  \nJiao Liu is with the College of Computing & Data Science, Nanyang Technological University, Singapore (e-mail: [jiao.liu@ntu.edu.sg](jiao.liu@ntu.edu.sg)).  \nYanchi Li is with the School of Computer Science, China University of Geosciences,(e-mail: int [lyc@cug.edu.cn](lyc@cug.edu.cn)).  \nHua Yu is with the School of Intelligent Rail Transportation, Dalian Jiaotong University, (e-mail: [yhiccd@163.com](yhiccd@163.com)).  \nAbhishek Gupta is with the School of Mechanical Sciences, Indian Institute of Technology, Goa, India. (e-mail: [abhishekgupta@iitgoa.ac.in](abhishekgupta@iitgoa.ac.in)).  \nYew-Soon Ong is with the College of Computing & Data Science, Nanyang Technological University, Singapore (e-mail: [asysong@ntu.edu.sg](asysong@ntu.edu.sg).  \naccelerating convergence and improving search performance relative to traditional evolutionary algorithms. Owing to these advantages, EMT has been widely applied to a variety of optimization tasks, including single-objective [6], [7], multiobjective [8]–[10], and combinatorial optimization tasks [11],[12] .  \nDespite the remarkable progress of EMT in recent years, most existing studies have primarily focused on improving optimization efficiency and enhancing convergence speed [4] . Specifica","cbCaiutnHpaVsyJq","https://ap.wps.com/l/cbCaiutnHpaVsyJq","pdf",16691620,1,23,"English","en",105,"# Abstract\n# Introduction\n## Evolutionary multitasking background\n## Expanding EMT beyond convergence acceleration\n## Novelty search motivation","[{\"question\":\"What limitation in existing evolutionary multitasking studies does this paper address?\",\"answer\":\"It moves beyond objective-driven optimization where inter-task consistency mainly speeds convergence toward predefined optima, instead targeting collaborative discovery of novel behaviors.\"},{\"question\":\"How does MFEA-CoD encourage tasks to find diverse novel behaviors?\",\"answer\":\"A multitask repulsion operator pushes different tasks to explore distinct regions of the unified search space, reducing redundant behavioral discoveries.\"},{\"question\":\"What problem does the novelty-augmented extension aim to solve?\",\"answer\":\"It jointly considers behavioral novelty with objective information to alleviate 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limitation in existing evolutionary multitasking studies does this paper address?","Question",{"text":75,"@type":76},"It moves beyond objective-driven optimization where inter-task consistency mainly speeds convergence toward predefined optima, instead targeting collaborative discovery of novel behaviors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does MFEA-CoD encourage tasks to find diverse novel behaviors?",{"text":80,"@type":76},"A multitask repulsion operator pushes different tasks to explore distinct regions of the unified search space, reducing redundant behavioral discoveries.",{"name":82,"@type":73,"acceptedAnswer":83},"What problem does the novelty-augmented extension aim to solve?",{"text":84,"@type":76},"It jointly considers behavioral novelty with objective information to alleviate premature convergence caused by deceptive 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