{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-interactive-evolution","title":"Deep Interactive Evolution","arxiv_id":"1801.08230","date":"2018-01-24","proceeding":null,"authors":["Philip Bontrager","Wending Lin","Julian Togelius","Sebastian Risi"],"abstract":"This paper describes an approach that combines generative adversarial\nnetworks (GANs) with interactive evolutionary computation (IEC). While GANs can\nbe trained to produce lifelike images, they are normally sampled randomly from\nthe learned distribution, providing limited control over the resulting output.\nOn the other hand, interactive evolution has shown promise in creating various\nartifacts such as images, music and 3D objects, but traditionally relies on a\nhand-designed evolvable representation of the target domain. The main insight\nin this paper is that a GAN trained on a specific target domain can act as a\ncompact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes\ndo resemble valid domain artifacts). Once such a GAN is trained, the latent\nvector given as input to the GAN's generator network can be put under\nevolutionary control, allowing controllable and high-quality image generation.\nIn this paper, we demonstrate the advantage of this novel approach through a\nuser study in which participants were able to evolve images that strongly\nresemble specific target images.","url_abs":"http://arxiv.org/abs/1801.08230v1","url_pdf":"http://arxiv.org/pdf/1801.08230v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-interactive-evolution","repo_url":"https://github.com/davidsvy/interactive-evolution","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}