A hybrid Retinex-based image enhancement algorithm is proposed to improve the quality of images captured by unmanned aerial vehicles (UAVs) in this paper. Hyperparameters of the employed multi-scale Retinex with chromaticity preservation (MSRCP) model are automatically tuned via a two-phase evolutionary computing algorithm. In the two-phase optimization algorithm, the Rao-2 algorithm is applied to performing the global search and a solution is obtained by maximizing the objective function. Next, the Nelder-Mead simplex method is used to improve the solution via local search. Real UAV-taken images of bad quality are collected to verify the performance of the proposed algorithm. Meanwhile, four famous image enhancement algorithms, Multi-Scale Retinex, Multi-Scale Retinex with Color Restoration, Automated Multi-Scale Retinex, and MSRCP are utilized as benchmarking methods. Meanwhile, two commonly used evolutionary computing algorithms, particle swarm optimization and flower pollination algorithm, are considered to verify the efficiency of the proposed method in tuning parameters of the MSRCP model. Experimental results demonstrate that the proposed method achieves the best performance compared with benchmarks and thus the proposed method is applicable for real UAV-based applications.

A method of color scheme is proposed considering contrast of luminance between adjacent regions and design property. This method aims at setting the contrast of luminance high, in order to make the image understandable to visually handicapped people. This method also realizes preferable color design for visually normal people by assigning color components from color combination samples. Interactive evolutionary computing is adopted to design the luminance and the color, so that the luminance and color components are assigned to each region appropriately on the basis of human subjective criteria. Here, the luminance is designed first, and then color components are assigned, keeping the luminance unchanged. Since samples of fine color combinations are applied, the obtained color design is also fine and harmonic. Computer simulations verify the high performance of this system.

A method to synthesize facial caricatures with non-planar expression is proposed. Several methods have been already proposed to synthesize facial caricatures automatically, but they mainly synthesize plane facial caricatures which look somewhat monotonous. In order to generate expressive facial caricature, the image should be expressed in non-planar style, expressing the depth of the face by shading and highlighting. In this paper, a new method to express such non-planar effect in facial caricatures is proposed by blending the grayscale information of the real face image into the plane caricature. Some methods also have been proposed to generate non-planar facial caricature, but the proposed method can adjust the degree of non-planar expression by interactive evolutionary computing, so that the obtained expression is satisfied by the user based on his/her subjective criteria. Since the color of the face looks changed, when the grayscale information of the natural face image is mixed, the color information of the skin area are also set by interactive evolutionary computing. Experimental results show the high performance of the proposed method.

A new type of digital filter for removing impulsive noise in color images is proposed using interactive evolutionary computing. This filter is realized as a rule-based system containing switching median filters. This filter detects impulsive noise in color images with rules and applies switching median filters only at the noisy pixel. Interactive evolutionary computing (IEC) is adopted to optimize the filter parameters, considering the subjective assessment by human vision. In order to detect impulsive noise precisely, complicated rules with multiple parameters are required. Here, the relationship between color components and the degree of peculiarity of the pixel value are utilized in the rules. Usually, optimization of such a complicated rule-based system is difficult, but IEC enables such optimization easily. Moreover, human taste and subjective sense are highly considered in the filter performance. Computer simulations are shown for noisy images to verify its high performance.

Wireless Internet technologies have been developing and users are now able to access more information anywhere through small screen mobile devices. However, due to the limits of cost, bandwidth and screen size in a wireless environment, it is important to minimize interactions between a mobile user and his handheld device, as well as the amount of data transmitted. In this paper we present an interactive evolutionary approach for user-oriented Web search by using mobile devices. To verify this approach, a series of experiments has been conducted. The results show that our approach can allocate the information a user needs within only a few user-system interactions. It substantially reduces the number of retrieved pages a user has to visit. This is especially an important benefit to mobile users.

Building robots is generally considered difficult, because the designer not only has to predict the interactions between the robot and the environment, but also has to deal with the consequent problems. In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory enough just to evolve the control system, because the performance of the control system depends on other hardware parameters -- the robot body plan -- which might include body size, wheel radius, motor time constant, etc. Therefore, the robot body plan itself should, ideally, also adapt to the task that the evolved robot is expected to accomplish. In this paper, a hybrid GP/GA framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it with a fixed robot body plan to evolve controllers for a variety of tasks at first, then to evolve complete robot systems. Experimental results show the promise of our system.