{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:55:33Z","timestamp":1761897333181,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":72,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["FA8750-15-C-0118"],"award-info":[{"award-number":["FA8750-15-C-0118"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014718","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-1618039, SHF-1652132, CCF-190863"],"award-info":[{"award-number":["CCF-1618039, SHF-1652132, CCF-190863"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006602","name":"Air Force Research Laboratory","doi-asserted-by":"publisher","award":["FA8750-19-1-0501"],"award-info":[{"award-number":["FA8750-19-1-0501"]}],"id":[{"id":"10.13039\/100006602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,7,8]]},"DOI":"10.1145\/3377929.3398139","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T21:02:12Z","timestamp":1594242132000},"page":"1849-1856","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["GEVO-ML"],"prefix":"10.1145","author":[{"given":"Jhe-Yu","family":"Liou","sequence":"first","affiliation":[{"name":"ASU"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"Facebook"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephanie","family":"Forrest","sequence":"additional","affiliation":[{"name":"ASU"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carole-Jean","family":"Wu","sequence":"additional","affiliation":[{"name":"ASU\/Facebook"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2018. XLA is a compiler that optimizes TensorFlow computations. https:\/\/www.tensorflow.org\/xla\/. (2018).  2018. XLA is a compiler that optimizes TensorFlow computations. https:\/\/www.tensorflow.org\/xla\/. (2018)."},{"volume-title":"Proc. of the 12th USENIX Conf. on Operating Systems Design and Implementation.","year":"2016","author":"Abadi Mart\u00edn","key":"e_1_3_2_1_2_1"},{"volume-title":"Tips to Improve Performance for Popular Deep Learning Frameworks on CPUs. Intel Developer Zone","year":"2018","author":"Anju P","key":"e_1_3_2_1_3_1"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2688500.2688521"},{"volume-title":"Automatic software diversity in the light of test suites. arXiv preprint arXiv:1509.00144","year":"2015","author":"Baudry Benoit","key":"e_1_3_2_1_5_1"},{"volume-title":"Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940","year":"2016","author":"Bello Irwan","key":"e_1_3_2_1_6_1"},{"key":"e_1_3_2_1_7_1","first-page":"281","article-title":"Random search for hyper-parameter optimization","author":"Bergstra James","year":"2012","journal-title":"Journal of Machine Learning Research 13"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2739480.2754752"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.1999.782671"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"volume-title":"Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274","year":"2015","author":"Chen Tianqi","key":"e_1_3_2_1_11_1"},{"volume-title":"Proc. of 13th {USENIX} Symp. on Operating Systems Design and Implementation.","year":"2018","author":"Chen Tianqi","key":"e_1_3_2_1_12_1"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2330784.2330799"},{"volume-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","year":"2002","author":"Deb Kalyanmoy","key":"e_1_3_2_1_14_1"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICST.2010.66"},{"key":"e_1_3_2_1_16_1","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. (2017). http:\/\/archive.ics.uci.edu\/ml  Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. (2017). http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_1_17_1","unstructured":"Facebook. 2018. Finding and Fixing Software Bugs Automatically With Sapfix and Sapienz. https:\/\/code.fb.com\/developer-tools\/finding-and-fixing-software-bugs-automatically-with-sapfix-and-sapienz\/. (2018).  Facebook. 2018. Finding and Fixing Software Bugs Automatically With Sapfix and Sapienz. https:\/\/code.fb.com\/developer-tools\/finding-and-fixing-software-bugs-automatically-with-sapfix-and-sapienz\/. (2018)."},{"volume-title":"https:\/\/caffe2.ai\/","year":"2019","key":"e_1_3_2_1_18_1"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1569901.1570031"},{"key":"e_1_3_2_1_20_1","unstructured":"Google. 2019. TensorFlow Performance Guide. https:\/\/docs.w3cub.com\/tensorflow~guide\/performance\/performance_guide\/#general_best_practices. (2019). TensorFlow Documentation.  Google. 2019. TensorFlow Performance Guide. https:\/\/docs.w3cub.com\/tensorflow~guide\/performance\/performance_guide\/#general_best_practices. (2019). TensorFlow Documentation."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"C. Le Goues T. Nguyen S. Forrest and W. Weimer. 2012. GenProg: A Generic Method for Automatic Software Repair. IEEE Transactions on Software Engineering (2012).  C. Le Goues T. Nguyen S. Forrest and W. Weimer. 2012. GenProg: A Generic Method for Automatic Software Repair. IEEE Transactions on Software Engineering (2012).","DOI":"10.1109\/TSE.2011.104"},{"volume-title":"Proc. of the Genetic and Evolutionary Computation Conf. Companion.","author":"Haraldsson Saemundur O.","key":"e_1_3_2_1_22_1"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3067695.3082517"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2018.00059"},{"key":"e_1_3_2_1_25_1","unstructured":"Chih-Wei Hsu Chih-Chung Chang Chih-Jen Lin etal 2003. A practical guide to support vector classification. (2003).  Chih-Wei Hsu Chih-Chung Chang Chih-Jen Lin et al. 2003. A practical guide to support vector classification. (2003)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.351"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359630"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2010.5585922"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2739480.2754652"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273556"},{"key":"e_1_3_2_1_33_1","unstructured":"Chris Lattner and Jacques Pienaar. 2019. MLIR Primer: A Compiler Infrastructure for the End of Moore's Law. (2019).  Chris Lattner and Jacques Pienaar. 2019. MLIR Primer: A Compiler Infrastructure for the End of Moore's Law. (2019)."},{"volume-title":"Proc. of the IEEE","year":"1998","author":"Cun Yann Le","key":"e_1_3_2_1_34_1"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2012.6227211"},{"volume-title":"Proc. of 2nd Annual Conf. on the Genetic and Evolutionary Computation Conf.","author":"Lee C.-Y.","key":"e_1_3_2_1_36_1"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/GI.2019.00014"},{"volume-title":"Conf. on Architectural Support for Programming Languages and Operating Systems.","year":"2019","author":"Liou Jhe-Yu","key":"e_1_3_2_1_38_1"},{"volume-title":"GEVO: GPU Code Optimization using EvolutionaryComputation.","year":"2020","author":"Liou Jhe-Yu","key":"e_1_3_2_1_39_1"},{"volume-title":"Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055","year":"2018","author":"Liu Hanxiao","key":"e_1_3_2_1_40_1"},{"key":"e_1_3_2_1_41_1","unstructured":"LLVM. 2020. Multi-Level IR Compiler Framework. (2020). https:\/\/mlir.llvm.org\/.  LLVM. 2020. Multi-Level IR Compiler Framework. (2020). https:\/\/mlir.llvm.org\/."},{"volume-title":"Overcoming the Equivalent Mutant Problem: A Systematic Literature Review and a Comparative Experiment of Second Order Mutation","year":"2014","author":"Madeyski Lech","key":"e_1_3_2_1_42_1"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/36206.36194"},{"volume-title":"Proc. of Intl. Conf. on Learning Representations.","year":"2016","author":"Molchanov Pavlo","key":"e_1_3_2_1_44_1"},{"key":"e_1_3_2_1_45_1","unstructured":"David J Montana and Lawrence Davis. 1989. Training Feedforward Neural Networks Using Genetic Algorithms.. In IJCAI.  David J Montana and Lawrence Davis. 1989. Training Feedforward Neural Networks Using Genetic Algorithms.. In IJCAI."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2908812.2908916"},{"key":"e_1_3_2_1_47_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017).  Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"John C. Platt. 1999. Advances in Kernel Methods. Chapter Fast Training of Support Vector Machines Using Sequential Minimal Optimization.  John C. Platt. 1999. Advances in Kernel Methods. Chapter Fast Training of Support Vector Machines Using Sequential Minimal Optimization.","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"e_1_3_2_1_49_1","unstructured":"Qualcomm. 2016. Snapdragon Neural Processing Engine SDK. (2016). https:\/\/developer.qualcomm.com\/docs\/snpe\/overview.html.  Qualcomm. 2016. Snapdragon Neural Processing Engine SDK. (2016). https:\/\/developer.qualcomm.com\/docs\/snpe\/overview.html."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2491956.2462176"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3305981"},{"volume-title":"Hogwild: A lock-free approach to parallelizing stochastic gradient descent. In Advances in neural information processing systems.","year":"2011","author":"Recht Benjamin","key":"e_1_3_2_1_53_1"},{"volume-title":"Glow: Graph Lowering Compiler Techniques for Neural Networks. arXiv preprint arXiv:1805.00907","year":"2018","author":"Rotem Nadav","key":"e_1_3_2_1_54_1"},{"key":"e_1_3_2_1_55_1","first-page":"6","article-title":"Online algorithms and stochastic approximations","volume":"5","author":"Saad David","year":"1998","journal-title":"Online Learning"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/2451116.2451151"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2541940.2541980"},{"volume-title":"Software Mutational Robustness. Genetic Programming and Evolvable Machines","year":"2014","author":"Schulte Eric","key":"e_1_3_2_1_59_1"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2739482.2768427"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2024156.2024186"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.58"},{"volume-title":"A hypercubebased encoding for evolving large-scale neural networks. Artificial life","year":"2009","author":"Stanley Kenneth O","key":"e_1_3_2_1_63_1"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1162\/106365602320169811"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431160802549278"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487629"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3067695.3082518"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/2001576.2001776"},{"volume-title":"Exploiting Parallelism Opportunities with Deep Learning Frameworks. arXiv preprint arXiv:1908.04705","year":"2019","author":"Wang Yu Emma","key":"e_1_3_2_1_69_1"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2009.5070536"},{"volume-title":"ThunderSVM: A Fast SVM Library on GPUs and CPUs. Journal of Machine Learning Research","year":"2018","author":"Wen Zeyi","key":"e_1_3_2_1_71_1"},{"volume-title":"2019 IEEE International Symposium on High Performance Computer Architecture (HPCA). 331--344","author":"Wu Carole-Jean","key":"e_1_3_2_1_72_1"},{"key":"e_1_3_2_1_73_1","unstructured":"Sixin Zhang Anna E Choromanska and Yann LeCun. 2015. Deep learning with elastic averaging SGD. In Advances in neural information processing systems. 685--693.  Sixin Zhang Anna E Choromanska and Yann LeCun. 2015. Deep learning with elastic averaging SGD. In Advances in neural information processing systems. 685--693."},{"volume-title":"Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578","year":"2016","author":"Zoph Barret","key":"e_1_3_2_1_74_1"}],"event":{"name":"GECCO '20: Genetic and Evolutionary Computation Conference","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"],"location":"Canc\u00fan Mexico","acronym":"GECCO '20"},"container-title":["Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3377929.3398139","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3377929.3398139","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3377929.3398139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:06Z","timestamp":1750200066000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3377929.3398139"}},"subtitle":["a proposal for optimizing ML code with evolutionary computation"],"short-title":[],"issued":{"date-parts":[[2020,7,8]]},"references-count":72,"alternative-id":["10.1145\/3377929.3398139","10.1145\/3377929"],"URL":"https:\/\/doi.org\/10.1145\/3377929.3398139","relation":{},"subject":[],"published":{"date-parts":[[2020,7,8]]},"assertion":[{"value":"2020-07-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}