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Nevertheless, numerous issues related to the instability of training need to be urgently addressed. Evolutionary computation (EC), using the corresponding paradigm of biological evolution, overcomes these problems and improves evolutionary-based GANs\u2019 ability to deal with real-world applications. Therefore, this paper presents a systematic literature survey combining EC and GANs. First, the basic theories of GANs and EC are analyzed and summarized. Second, to provide readers with a comprehensive view, this paper outlines the recent advances in combining EC and GANs after detailed classification and introduces each of them. These classifications include evolutionary GANs and their variants, GANs with evolutionary strategies and differential evolution, GANs combined with neuroevolution, evolutionary GANs related to different optimization problems, and applications of evolutionary GANs. Detailed information on the evaluation metrics, network structures, and comparisons of these models is presented in several tables. Finally, future directions and possible perspectives for further development are discussed.<\/jats:p>","DOI":"10.1007\/s10462-024-10818-y","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T03:36:50Z","timestamp":1718941010000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey"],"prefix":"10.1007","volume":"57","author":[{"given":"Yong","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gai-Ge","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honglei","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"issue":"1","key":"10818_CR1","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s13321-022-00623-6","volume":"14","author":"M Abbasi","year":"2022","unstructured":"Abbasi M, Santos BP, Pereira TC, Sofia R, Monteiro NR, Sim\u00f5es CJ, Brito RM, Ribeiro B, Oliveira JL, Arrais JP (2022) Designing optimized drug candidates with generative adversarial network. 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