Experimental Comparison of Human-based Evolutionary Computation and ChatGPT

Abstract

In recent years, generative AI, which is a mechanism for automatically generating content that meets human needs, has been attracting attention. There are a wide variety of types of generated content, and ChatGPT is a typical example of a generated AI that responds to requests from humans using natural language. Generative AI acquires generation methods based on automatic learning of past data. On the other hand, people have been gathering their knowledge, experience, and creativity to solve various problems on the Web. Q&A sites are a typical example. Furthermore, in the field of evolutionary computation, there is human-based evolutionary computation, in which a group of humans solves problems by performing evolutionary computation. Now that generative AI has appeared, it is necessary to identify problems for which problem-solving methods based solely on the power of human groups are effective. Therefore, in this study, we compare the problem-solving performance of ChatGPT, a generative AI, and human-based evolutionary computation based only on people’s power, for two types of problems. The two types of problems are problems for which the Web is full of information that can help solve problems, and problems for which there is little such problem on the Web. The experimental results suggest that ChatGPT is more suitable for the former type of problem and the human-based evolutionary computation is more suitable for the latter type of problem.