Complexity and Aesthetics in Generative and Evolutionary Art
Jon McCormack, Camilo Cruz Gambardella
备注：
Preprint of paper submitted to the Journal Genetic Programming and Evolvable Machines. arXiv admin note: substantial text overlap with arXiv:2102.02332
In this paper we examine the concept of complexity as it applies to generative and evolutionary art and design. Complexity has many different, discipline specific definitions, such as complexity in physical systems (entropy), algorithmic measures of information complexity and the field of "complex systems". We apply a series of different complexity measures to three different evolutionary art datasets and look at the correlations between complexity and individual aesthetic judgement by the artist (in the case of two datasets) or the physically measured complexity of generative 3D forms. Our results show that the degree of correlation is different for each set and measure, indicating that there is no overall "better" measure. However, specific measures do perform well on individual datasets, indicating that careful choice can increase the value of using such measures. We then assess the value of complexity measures for the audience by undertaking a large-scale survey on the perception of complexity and aesthetics. We conclude by discussing the value of direct measures in generative and evolutionary art, reinforcing recent findings from neuroimaging and psychology which suggest human aesthetic judgement is informed by many extrinsic factors beyond the measurable properties of the object being judged.
EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization
Southern University of Science and Technology（南方科技大学）；Research Institute of Trustworthy Autonomous Systems（可信人工智能研究院）；Department of Computer Science and Engineering（计算机科学与工程系）；Pengcheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；The Hong Kong Polytechnic University（香港理工大学）；Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；Hong Kong Polytechnic University-Daya Bay Technology and Innovation Research Institute（香港理工大学-大湾技术创新研究院）；Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation（广东省脑启发智能计算重点实验室）
University of Technology Sydney（悉尼科技大学）；The University of Sydney（悉尼大学）；Southern University of Science and Technology（南方科技大学）；University of Birmingham（伯明翰大学）
Evolutionary Computation: A Paradigm of Natural Generative AI and Creative Synthesis
Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR), Singapore（前沿人工智能研究中心（CFAR），科技研究局（A*STAR），新加坡）；School of Mechanical Sciences, Indian Institute of Technology (IIT) Goa（机械科学学院，印度理工学院（IIT） Goa）；School of Computer Science and Technology, Dalian University of Technology (DLUT)（计算机科学与技术学院，大连理工大学）；College of Computing and Data Science (CCDS), Nanyang Technological University (NTU)（计算与数据科学学院（CCDS），南洋理工大学）；Honda Research Institute Europe (HRI-EU), Offenbach am Main, Germany（本田欧洲研究院（HRI-EU），德国Offenbach am Main）；Data Science and Artificial Intelligence Research Centre, School of Computer Science and Engineering, Nanyang Technological University (NTU)（数据科学与人工智能研究中心，计算机科学与工程学院，南洋理工大学）
Allen Discovery Center at Tufts University（塔夫茨大学艾伦发现中心）；Institute for Theoretical Physics, TU Wien（维也纳技术大学理论物理研究所）；Wyss Institute for Biologically Inspired Engineering at Harvard University（哈佛大学威斯生物启发工程研究所）
LLM2TEA: An Agentic AI Designer for Discovery with Generative Evolutionary Multitasking
1 College of Computing \& Data Science (CCDS), Nanyang Technological University (NTU), Singapore 2 Honda Research Institute Europe (HRI-EU), Offenbach am Main, Germany 3 Centre for Frontier AI Research (CFAR), Agency for Science, Technology
