This paper proposes a novel application of coded apertures (CAs) for visual information hiding. CA is one of the representative computational photography techniques, in which a patterned mask is attached to a camera as an alternative to a conventional circular aperture. With image processing in the post-processing phase, various functions such as omnifocal image capturing and depth estimation can be performed. In general, a watermark embedded as high-frequency components is difficult to extract if captured outside the focal length, and defocus blur occurs. Installation of a CA into the camera is a simple solution to mitigate the difficulty, and several attempts are conducted to make a better design for stable extraction. On the contrary, our motivation is to design a specific CA as well as an information hiding scheme; the secret information can only be decoded if an image with hidden information is captured with the key aperture at a certain distance outside the focus range. The proposed technique designs the key aperture patterns and information hiding scheme through evolutionary multi-objective optimization so as to minimize the decryption error of a hidden image when using the key aperture while minimizing the accuracy when using other apertures. During the optimization process, solution candidates, i.e., key aperture patterns and information hiding schemes, are evaluated on actual devices to account for disturbances that cannot be considered in optical simulations. Experimental results have shown that decoding can be performed with the designed key aperture and similar ones, that decrypted image quality deteriorates as the similarity between the key and the aperture used for decryption decreases, and that the proposed information hiding technique works on actual devices.

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

We investigate an interactive evolutionary computation (IEC) using multiple users' gaze information when users partially participate in each design evaluation. Many previous IEC systems have a problem that user evaluation loads are too large. Hence, we proposed to employ user gaze information for evaluating designs generated by IEC systems in order to solve this problem. In this proposed system, users just view the presented designs, not assess, then the system automatically creates users' favorite designs. With the user's gaze information, the proposed system generates coordination that can satisfy many users. In our previous study, we verified the effectiveness of the proposed system from a real system operation viewpoint. However, we did not consider the fluctuation of the users during a solution candidate evaluation. In the actual operation of the proposed system, users may change during the process due to the user interchange. Therefore, in this study, we verify the effectiveness of the proposed system when varying the users participating in each evaluation for each generation. In the experiment, we employ two types of situations as assumed in real environments. The first situation changes the number of users evaluating the designs for each generation. The second situation employs various users from the predefined population to evaluate the designs for each generation. From the experimental results in the first situation, we confirm that, despite the change in the number of users during the solution candidate evaluation, the proposed system can generate coordination to satisfy many users. Also, from the results in the second situation, we verify that the proposed system can also generate coordination which both users who participate in the coordination evaluation can more satisfy.

Although Wolf Pack Algorithm (WPA) is a novel optimal algorithm with good performance, there is still room for improvement with respect to its convergence. In order to speed up its convergence and strengthen the search ability, we improve WPA with the Differential Evolution (DE) elite set strategy. The new proposed algorithm is called the WPADEES for short. WPADEES is faster than WPA in convergence, and it has a more feasible adaptability for various optimizations. Six standard benchmark functions are applied to verify the effects of these improvements. Our experiments show that the performance of WPADEES is superior to the standard WPA and other intelligence optimal algorithms, such as GA, DE, PSO, and ABC, in several situations.

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.

We present an Interactive Tabu Search (ITS) algorithm to reduce the evaluation load of Interactive Evolutionary Computation (IEC) users. Most previous IEC studies used an evaluation interface that required users to provide evaluation values for all candidate solutions. However, user's burden with such an evaluation interface is large. Therefore, we propose ITS where users choose the favorite candidate solution from the presented candidate solutions. Tabu Search (TS) is recognized as an optimization technique. ITS evaluation is simpler than Interactive Genetic Algorithm (IGA) evaluation, in which users provide evaluation values for all candidate solutions. Therefore, ITS is effective for reducing user evaluation load. We evaluated the performance of our proposed ITS and a Normal IGA (NIGA), which is a conventional 10-stage evaluation, using a numerical simulation with an evaluation agent that imitates human preferences (Kansei). In addition, we implemented an ITS evaluation for a running-shoes-design system and examined its effectiveness through an experiment with real users. The simulation results showed that the evolution performance of ITS is better than that of NIGA. In addition, we conducted an evaluation experiment with 21 subjects in their 20 s to assess the effectiveness of these methods. The results showed that the satisfaction levels for the candidates generated by ITS and NIGA were approximately equal. Moreover, it was easier for test subjects to evaluate candidate solutions with ITS than with NIGA.

An interactive support system for image quality enhancement to adjust display equipments according to the user's own subjectivity is developed. Interactive support system for image quality enhancement enable the parameters based on user's preference to be derived by only selecting user's preference images without adjusting image quality parameters directly. In the interactive support system for image quality enhancement, the more the number of parameters is, the more effective this system is. In this paper, lightness, color and sharpness are used as the image quality parameters and the images are enhanced by increasing the number of parameters. Shape of tone curve is controlled by two image quality adjustment parameters for lightness enhancement. Images are enhanced using two image quality adjustment parameters for color enhancement. The two parameters are controlled in L* a* b* color space. Degree and coarseness of image sharpness enhancement are adjusted by controlling a radius of mask of smoothing filter and weight of adding. To confirm the effectiveness of the proposed method, the image quality and derivation time of the proposed method are compared with a manual adjustment method.

In the present paper, we propose an evolutionary P2P networking technique that dynamically and adaptively optimizes several P2P network topologies, in which all of the nodes are included at the same time, in an evolutionary manner according to given evaluation criteria. In addition, through simulations, we examine whether the proposed evolutionary P2P networking technique can provide reliable search capability in dynamic P2P environments. In simulations, we assume dynamic P2P environments in which each node leaves and joins the network with its own probability and in which search objects vary with time. The simulation results show that topology reconstruction by the evolutionary P2P networking technique is better than random topology reconstruction when only a few types of search objects are present in the network at any moment and these search objects are not replicated. Moreover, for the scenario in which the evolutionary P2P networking technique is more effective, we show through simulations that when each node makes several links with other nodes in a single network topology, the evolutionary P2P networking technique improves the reliable search capability. Finally, the number of links that yields more reliable search capability appears to depend on how often nodes leave and join the network.

This paper considers Cyclic Job-Shop Scheduling Problems (CJSSP) extended from the Job-Shop Scheduling Problem (JSSP). We propose an evolutionary computing method to solve the problem approximately by generating the Petri net structure for scheduling. The crossover proposed in this paper employs structural analysis of Petri net model, that is, the crossover improves the cycle time by breaking the bottle-neck circuit obtained by solving a linear programming problem. Experimental evaluation shows the effectiveness of our approach.

An efficient clustering strategy for estimation of distribution algorithms (EDAs) is presented. It is used for properly fitting probabilistic models that play an important role in guiding search direction. To this end, a fitness-aided ordering scheme is devised for deciding the input sequence of samples (i.e., individuals) for clustering. It can effectively categorise the individuals by using the (available) information about fitness landscape. Moreover, a virtual leader is introduced for providing a reliable reference for measuring the distance from samples to its own cluster. The proposed algorithm incorporates them within the framework of random the leader algorithm (RLA). Experimental results demonstrate that the proposed approach is more effective than the existing ones with regard to probabilistic model fitting.

Effective trading with given pattern-based multi-predictors of stock price needs an intelligent asset allocation strategy. In this paper, we study a method of dynamic asset allocation, called the meta policy, which decides how much the proportion of asset should be allocated to each recommendation for trade. The meta policy makes a decision considering both the recommending information of multi-predictors and the current ratio of stock funds over the total asset. We adopt evolutionary computation to optimize the meta policy. The experimental results on the Korean stock market show that the trading system with the proposed meta policy outperforms other systems with fixed asset allocation methods.

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the face search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to handle it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experiments on a set of 42 test images show the effectiveness of the proposed approach. Moreover, the effect of PSO parameter settings on the search performance was investigated.

This paper presents an efficient graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG) and its extension to a parallel version. A new version of parallel EGG system is based on a coarse-grained model of parallel processing and can synthesize heterogeneous networks of various different components efficiently. The potential capability of parallel EGG system is demonstrated through the design of current-mode logic circuits.

This letter presents an efficient graph-based evolutionary optimization technique, and its application to the transistor-level design of multiple-valued arithmetic circuits. The key idea is to introduce "circuit graphs with colored terminals" for modeling heterogeneous networks of various components. The potential of the proposed approach is demonstrated through experimental synthesis of a radix-4 signed-digit (SD) full adder circuit.

This paper proposes distributed evolutionary digital filters (EDFs) as an improved version of the original EDF. The EDF is an adaptive digital filter which is controlled by adaptive algorithm based on evolutionary computation. In the proposed method, a large population of the original EDF is divided into smaller subpopulations. Each sub-EDF has one subpopulation and executes the small-sized main loop of the original EDF. In addition, the distributed algorithm periodically selects promising individuals from each subpopulation. Then, they migrate to different subpopulations. Numerical examples show that the distributed EDF has a higher convergence rate and smaller steady-state value of the square error than the LMS adaptive digital filter, the adaptive digital filter based on the simple genetic algorithm and the original EDF.

This paper presents an efficient graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG), and its application to the design of fast constant-coefficient multipliers using parallel counter-tree architecture. An important feature of EGG is its capability to handle the general graph structures directly in evolution process instead of encoding the graph structures into indirect representations, such as bit strings and trees. This paper also addresses the major problem of EGG regarding the significant computation time required for verifying the function of generated circuits. To solve this problem, a new functional verification technique for arithmetic circuits is proposed. It is demonstrated that the EGG system can create efficient multiplier structures which are comparable or superior to the known conventional designs.

This paper presents a new approach to designing arithmetic circuits by using a graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG). The key idea of the proposed method is to introduce a higher level of abstraction for arithmetic algorithms, in which arithmetic circuit structures are modeled as data-flow graphs associated with specific number representation systems. The EGG system employs evolutionary operations to transform the structure of graphs directly, which makes it possible to generate the desired circuit structure efficiently. The potential capability of EGG is demonstrated through an experiment of generating constant-coefficient multipliers.

This paper proposes an automatic structural programming system. Genetic Programming achieves success for automatic programming using the evolutionary process. However, the approach doesn't deal with the essential program concept in the sense of what is called a program in software science. It is useful that a program be structured by various sub-structures, i. e. subroutines, however, the above-mentioned approach treats a single program as one sequence. As a result of the above problem, there is a lack of reusability, flexibility, and a decreases in the possibility of use as a utilitarian programming system. In order to realize a structural programming system, this paper proposes a method which can generate a program constructed by subroutines, named formula, using the evolutionary process.

The problem of genetic algorithm's efficiency has been attracting the attention of genetic algorithm community. Over the last decade, considerable researches have focused on improving genetic algorithm's performance. However, they are generally under the framework of natural evolutionary mechanism and the major genetic operators, crossover and mutation, are activated by the prior probabilities. An operator based on a prior probability possesses randomness, that is, the unexpected individuals are frequently operated, but the expected individuals are sometimes not operated. Moreover, as the evaluation function is the link between the genetic algorithm and the problem to be solved, the evaluation function provides the heuristic information for evolutionary search. Therefore, how to use this kind of heuristic information (present and past) is influential in the efficiency of evolutionary search. This paper, as an attempt, presents a eugenics-based genetic algorithm (EGA) -- a genetic algorithm that reflects the human's decision will (eugenics), and fully utilizes the heuristic information provided by the evaluation function for the decisions. In other words, EGA = evolutionary mechanisms + human's decision will + heuristic information. In EGA, the ideas of the positive eugenics and the negative eugenics are applied as the principle of selections and the selections are not activated by the prior probabilities but by the evaluation values of individuals. A method of genealogical chain-based selection for mutation is proposed, which avoids the blindness of stochastic mutation and the disruptive problem of mutation. A control strategy of reasonable competitions is proposed, which brings the effects of crossover and mutation into full play. Three examples, the minimum problem of a standard optimizing function--De Jong's test function F2, a typical combinatorial optimization problem--the traveling salesman problem, and a problem of identifying nonlinear system, are given to show the good performance of EGA.