Superiority demonstration of variance-considered machines by comparing error rate with support vector machines

To improve the performance of classification algorithms, we proposed a new varianceconsidered machine (VCM) classification algorithm in a previous study. The study showed theoretically that VCMs have lower error probabilities than SVMs. The purpose of this paper is to experimentally demonstrate the superiority of VCMs. Therefore, we verified our proposal with several case experiments using data following a Gaussian distribution with different variances and prior probabilities. To estimate performance, the experiment for each case was executed 1000 times and the error rates were averaged for accuracy. The data of each experiment have different distances between means of data, and different ratios between training data and testing data. Thus, we proved that the error rate of VCMs is lower than the error rate of SVMs, although their performances were not similar in each case. Consequently, we expect that VCMs will be applied to a variety fields.     

Game model-based co-evolutionary algorithm with non-dominated memory and Euclidean distance selection mechanisms for multi-objective optimization

Many real-world problems involve simultaneous optimization of several incommensurable and often competing objectives. In the search for solutions to multi-objective optimization problems (MOPs), we find that there is no single optimum but rather a set of optimums known as the “Pareto optimal set”. Co-evolutionary algorithms are well suited to optimization problems which involve several often competing objectives. Co-evolutionary algorithms are aimed at evolving individuals through individuals competing in an objective space. In order to approximate the ideal Pareto optimal set, the search capability of diverse individuals in an objective space can be used to determine the performance of evolutionary algorithms. Non-dominated memory and Euclidean distance selection mechanisms for co-evolutionary algorithms have the goal of overcoming the limited search capability of diverse individuals in the population space. In this paper, we propose a method for maintaining population diversity in game model-based co-evolutionary algorithms, and we evaluate the effectiveness of our approach by comparing it with other methods through rigorous experiments on several MOPs.