Performance Comparison between Human-based Evolutionary Computation and Generative AI through Experiments

Abstract

In the field of evolutionary computation, there is human-based evolutionary computation (HBEC), 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 mainly on the power of human groups as HBEC are effective. Therefore, this study experimentally compares the problem-solving performance of ChatGPT, a generative AI, and HBEC system for two types of problems. One type of problems are problems involving many people in society. The other type of problems are problems related to specific communities. Our previous study conducted the same experiment, but there was a defect in that experiment. Therefore, a similar experiment is conducted again in this study. The experimental results suggest that ChatGPT is more suitable for the former type of problem and HBEC system is more suitable for the latter type of problem. This suggestion is consistent with that of our previous study.