Abstract The basic idea in the estimation of distribution algorithms is the replacement of heuristic operators with machine learning models such as regression models, clustering models, or classification models. So, recently, the model‐based evolutionary algorithms (MBEAs) have been suggested in three groups: The estimation of distribution algorithms (EDAs), surrogate assisted evolutionary algorithms, and the inversed models to map from the objective space to the decision space. In this article, a new approach, based on an inversed model of Gaussian process and random forest framework, is proposed. The main idea is applying the process of random forest variable importance with a random grouping that determines some of the best assignment of decision variables to objective functions in order to form a Gaussian process in inverse models that maps to decision space the rich solutions which are discovered from objective space. Then these inverse models through sampling the objective space generate offspring. The proposed algorithm has been tested on the benchmark test suite for evolutionary algorithms (modified Deb K, Thiele L, Laumanns M, Zitzler E (DTLZ), and Walking Fish Group (WFG)) and indicates that the proposed method is a competitive and promising approach.

Abstract Most existing methods of multiobjective estimation of distributed algorithms apply the estimation of distribution of the Pareto‐solution on the decision space during the search and little work has proposed on making a regression‐model for representing the final solution set. Some inverse‐model‐based approaches were reported, such as inversed‐model of multiobjective evolutionary algorithm (IM‐MOEA), where an inverse functional mapping from Pareto‐Front to Pareto‐solution is constructed on nondominated solutions based on Gaussian process and random grouping technique. But some of the effective inverse models, during this process, may be removed. This paper proposes an inversed‐model based on random forest framework. The main idea is to apply the process of random forest variable importance that determines some of the best assignment of decision variables ( x n ) to objective functions ( f m ) for constructing Gaussian process in inversed‐models that map all nondominated solutions from the objective space to the decision space. In this work, three approaches have been used: classical permutation, Naïve testing approach, and novel permutation variable importance. The proposed algorithm has been tested on the benchmark test suite for evolutionary algorithms [modified Deb K, Thiele L, Laumanns M, Zitzler E (DTLZ) and Walking Fish Group (WFG)] and indicates that the proposed method is a competitive and promising approach.

Characters connectivity is a problem in automated printed Farsi/Arabic script recognition. This paper introduces a novel scheme based on wavelet transform to solve segmentation of printed Farsi/Arabic words into characters. Our novel algorithm employs a new wavelet transform by which the extracted wavelet coefficients are exploited, in detecting, underlying horizontal edges and base line. Projection of horizontal edges and their location on base line provide the segmentation points. A classification method distinguishes true segmenting points. New algorithm is robust against noise, gray level, font and size of characters. Simulation results provide a comparison between new algorithm and three schemes, closed contour, structural and holistic, in terms of precision, speed and robustness against Gaussian noise. Experimental Results indicate superiority of our scheme in terms of precision and show that new algorithm improves recognition speed by a factor of at least 2.5 times.

This paper presents a novel holistic handwritten Farsi /Arabic word recognition scheme in situation where we face with word rotation and scale change. Image words features are extracted by exploiting rotation and scale invariance characteristics of M-Band packet wavelet transform performed on polar transform version of images of handwritten Farsi/Arabic words. The extracted features construct a feature vector for each word image. This vector is employed in recognition phase by finding the similar words based on the least Mahalanobis distance of feature vectors. This scheme is robust against rotation and scaling. Experimental results, obtained from testing different handwritten texts with various orientations and scales, show that proposed scheme outperforms Fourier-wavelet and Zernike moments algorithms. The robustness of new scheme has been tested with images corrupted by Gaussian noise and compared with similar schemes. Experimental results show that the accuracy of our algorithm reaches 95.8 percents.