[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124589-en":3,"doc-seo-124589-105":30,"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":4,"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":27,"seo_description":14,"update_tm":28,"read_time":29},124589,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",6,"Technology","EC-KitY - Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration","EC-KitY presents an evolutionary computation tool kit for evolutionary algorithms that integrates smoothly with machine learning workflows in Python. It motivates evolutionary algorithms as gradient-free search methods suited to design-from-scratch problems, limited human expertise, interpretable representations, and multiobjective optimization. The library is described as extensible via constructors, wrapped for sklearn compatibility, and engineered with hooks supporting cloud/cluster execution, multithreaded evaluation, statistics, and replicability. It also compares EC-KitY with other major packages via feature tables.","arXiv :2207 . 10367v2 [ cs .NE] 19 Apr 2023  \nEC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration  \nMoshe Sippera , Tomer Halperina , Itai Tzruiaa , Achiya Elyasafb  \na Department of Computer Science, Ben-Gurion University of the  \nNegev, Beer-Sheva, 8410501, Israel  \nb Department of Software and Information Systems Engineering, Ben-Gurion University  \nof the Negev, Beer-Sheva, 8410501, Israel  \n1. Introduction  \nIn Evolutionary Computation (EC)—or Evolutionary Algorithms (EAs)—core concepts from evolutionary biology—inheritance, random variation, and selection—are harnessed in algorithms that are applied to complex computational problems. As discussed by Sipper et al. [1], EAs present several important bene􀀌ts over popular machine learning (ML) methods, including: less reliance on the existence of a known or discoverable gradient within the search space; ability to handle design problems, where the objective is to design new entities from scratch; fewer required a priori assumptions about the problem at hand; seamless integration of human expert knowledge; ability to solve problems where human expertise is very limited; support of interpretable solution representations; support of multiple objectives.  \nPreprint submitted to SoftwareX April 20, 2023  \nThe user may wish to specify additional components of the EA rather than rely on default values; this can be readily achieved through the relevant constructor, e.g.:  \nalgo = Simple Evolution (  \nSub population ( creators =  \nRamp ed Half And Half Creator ( init_depth =(2 , 4) ,  \nterminal_set = terminal_set , function_set = function_set , bloat_weight =0 . 0001) ,  \npopulation_size =200 ,  \nevaluator = Symbolic Regression Eva lua tor () ,  \nhigher_ is_better = False ,  \nelitism_rate =0 . 05 ,  \noperators_sequence =[  \nSubtree Crossover ( probability =0 . 9 , arity =2) ,  \nSubtree Mutation ( probability =0 . 2 , arity =1) ,  \nERCMutation ( probability =0 . 05 , arity =1)  \n],  \n) ,  \nbreeder = Simple Breeder () ,  \nmax_workers =4 ,  \nmax_generation =500 ,  \nstatistics = Best Average Worst Statistics () ,  \ntermination_checker =  \n|  | Abstract class Association |  |  | Class generalized by Generalization | Operator |\n| --- | --- | --- | --- | --- | --- |\n\n(Figure 1, right) . The latter is achieved by wrapping an Algorithm instance with an SKLearnWrapper.  \nis designed to be easily extended: it is heavily over-architected, with many hooks that facilitate system modi􀀌cation and enhancement. The architecture supports the following features: execution in the cloud or in a cluster, multiple statistics, multithreaded evaluation, replicability standards (all of which have already been implemented), as well as checkpointing and logging facilities (under active development) .  \n3. Comparison with Other Packages  \nThrough considerable hands-on experience with EC open-source software over many years, and through additional extensive research, we have identi-􀀌ed the following eight tools, whose major features are compared in Table 1, along with [10]: [12], [13], [14],  \n[15], [11], [16], [17], [18] .  \nOnly two packages are written in Python and are also sklearn-compatible: and . However, as shown in Table 1, they lack many im  \nportant features that possesses.  \n4. Concluding Remarks  \nThe library is under active development. Presently, we plan to  \nadd a variety of evolutionary algorithms, individual types, genetic operators,  \nEC-KitY  \nECJ  \nEC-KitYJeneti􀁣s  \ngplearn KEEL  \nDEAP  \ngeatpy  \nHeuristi􀁣Lab  \nPlatypus  \ngeatpy  \ngplearn  \nEC-KitY  \nEC-KitY  \n| Feature | EC-KitY | geatpy | gplearn | DEAP | Platypus | ECJ | Jeneti􀁣s | KEEL | Heuristi􀁣Lab |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Language | Python | Python | Python | Python | Python | Java | Java | Java | C\\# |\n| sklearn-compatible | ✔ | ✔ | ✔ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |\n| SE Design | ✔ | ∃ | ✗ | ✗ | ✗ | ✔ | ✔ | ∃ | ✔ |\n| GA representations | ✔ | ✔ | ✗ | ✔ | ✔ | ","cbCaiudRyla9nUWY","https://ap.wps.com/l/cbCaiudRyla9nUWY","pdf",156283,1,10,"English","en",105,"# Introduction\n# Comparison with Other Packages\n# Concluding Remarks","[{\"question\":\"Why use evolutionary computation over popular machine learning methods?\",\"answer\":\"Evolutionary algorithms reduce reliance on known gradients, handle design problems without predefined gradients, require fewer prior assumptions, support interpretable representations, and can optimize multiple objectives.\"},{\"question\":\"How does EC-KitY support extensibility and configuration?\",\"answer\":\"EC-KitY can accept additional components through constructors, including creator, breeder, evaluator, operator sequences, worker and generation limits, statistics, and termination checkers.\"},{\"question\":\"How is EC-KitY positioned compared with other evolutionary computation packages?\",\"answer\":\"The document compares major tools by features such as Python support, sklearn compatibility, representation support, user-defined operators, coevolution, multiobjective ability, statistics, documentation, API coverage, and latest version.\"}]","EC-KitY - Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration | PDF",1785893198,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ec-kity-evolutionary-computation-tool-kit-in-python-with-seamless-machine-learning-integration","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ec-kity-evolutionary-computation-tool-kit-in-python-with-seamless-machine-learning-integration/124589/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why use evolutionary computation over popular machine learning methods?","Question",{"text":75,"@type":76},"Evolutionary algorithms reduce reliance on known gradients, handle design problems without predefined gradients, require fewer prior assumptions, support interpretable representations, and can optimize multiple objectives.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does EC-KitY support extensibility and configuration?",{"text":80,"@type":76},"EC-KitY can accept additional components through constructors, including creator, breeder, evaluator, operator sequences, worker and generation limits, statistics, and termination checkers.",{"name":82,"@type":73,"acceptedAnswer":83},"How is EC-KitY positioned compared with other evolutionary computation packages?",{"text":84,"@type":76},"The document compares major tools by features such as Python support, sklearn compatibility, representation support, user-defined operators, coevolution, multiobjective ability, statistics, documentation, API coverage, and latest version.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]