{"id":5768,"date":"2025-07-24T16:37:14","date_gmt":"2025-07-24T13:07:14","guid":{"rendered":"https:\/\/arrahimipour.com\/articles-en\/applying-machine-learning-to-optimize-software-runtime-and-memory-usage\/"},"modified":"2026-03-26T18:13:20","modified_gmt":"2026-03-26T14:43:20","slug":"applying-machine-learning-to-optimize-software-runtime-and-memory-usage","status":"publish","type":"post","link":"https:\/\/arrahimipour.com\/en\/articles-en\/applying-machine-learning-to-optimize-software-runtime-and-memory-usage\/","title":{"rendered":"Applying Machine Learning to Optimize Software Runtime and Memory Usage"},"content":{"rendered":"<p dir=\"ltr\">Software systems today face increasing demands for high performance and efficient memory usage. Traditional optimization methods, often hard-coded and inflexible, struggle to adapt to complex and dynamic workloads. Machine Learning (ML) algorithms offer a promising approach by automatically learning patterns and making intelligent decisions to optimize execution time (runtime) and memory consumption. This paper provides a comprehensive review of recent research (2021\u20132025) on applying ML techniques \u2013 including neural networks, reinforcement learning, and evolutionary algorithms \u2013 to performance optimization in software systems. We discuss how ML-driven solutions have achieved significant improvements, such as reducing program execution time, enhancing memory\/cache efficiency, and intelligently allocating resources in cloud environments.<\/p>\n<p dir=\"ltr\">Article Link:<\/p>\n<p dir=\"ltr\"><a href=\"https:\/\/civilica.com\/doc\/2317319\/\">https:\/\/civilica.com\/doc\/2317319\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Software systems today face increasing demands for high performance and efficient memory usage. Traditional optimization methods, often hard-coded and inflexible, struggle to adapt to complex and dynamic workloads. Machine Learning (ML) algorithms offer a promising approach by automatically learning patterns and making intelligent decisions to optimize execution time (runtime) and memory consumption. This paper provides [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":5736,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[39],"tags":[64,67,66],"class_list":["post-5768","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles-en","tag-ai-development","tag-ai-optimization","tag-machine-learning"],"acf":[],"_links":{"self":[{"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/posts\/5768","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/comments?post=5768"}],"version-history":[{"count":0,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/posts\/5768\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/media\/5736"}],"wp:attachment":[{"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/media?parent=5768"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/categories?post=5768"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/tags?post=5768"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}