We propose an interactive image browsing system which combines similarity-based image retrieval technology and simulated breeding methodology. Here "simulated breeding methodology" refers to the generic framework of human interface for the interactive optimization of subjective problems. Based on this framework, the scenario of interactive image browsing is summarized as follows: (1)A user is first requested to determine the first example image before starting image browsing. (2)The system next retrieves similar images to the given example image by applying current image retrieval parameters. (3)Image retrieved in high orders are displayed on the screen for the user to give some feedback to the system. (4)A significance point to each image given by the user, which is the form of feedback employed in this paper, is transformed into the fitness of individuals; then the underlying optimization algorithm, namely queue-based genetic algorithm(QGA) proposed in this paper as an appropriate algorithm for simulated breeding methodology, plays the role of interactive optimization of image retrieval parameters. To realize such a system, graphical user interface is constructed for the satellite cloud image database that archives 1027 images, where the image representation model used for extracting and indexing image contents is called "hierarchical model of image content elements". Results are analyzed based on the history of image browsing, and they demonstrate steady improvement in terms of the similarity retrieval order of the target image, because of the sequential change of example images and the optimization of image retrieval parameters by QGA.