G. A. Vijayalakshmi Pai

Bio: G. A. Vijayalakshmi Pai is an academic researcher from PSG College of Technology. The author has contributed to research in topics: Portfolio optimization & Portfolio. The author has an hindex of 11, co-authored 28 publications receiving 851 citations.

Neural networks, fuzzy logic, and genetic algorithms : synthesis and applications

Abstract: A Study of Adaptive Neural Network Control System. Zhong, Heng Design of Fuzzy Logic Controller Based on Differential Evolution Algorithm. Shuai, Li (et al.). Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis. Fuzzy Logic and Neural Networks: Basic Concepts and Applications. logic genetic by rajasekaran ebook. srajasekaran and ga vijayalakshmi pai neural networks. MODERN MAGNETIC MATERIALS PRINCIPLES AND APPLICATIONS PDF FREE NETWORKS FUZZY LOGIC AND GENETIC ALGORITHMS SYNTHESIS.

Evolutionary Optimization of Constrained $k$ -Means Clustered Assets for Diversification in Small Portfolios

Abstract: The problem of portfolio optimization has been rendered complex for direct solving by traditional and numerical approaches when constraints that model investor preferences and/or market friction are included in the mathematical model, and for such cases, heuristic approaches have been sought for their solution. In this paper, we discuss the solution of a subclass of portfolio optimization problems, which include basic, bounding, cardinality, and class constraints in its fold, with the investor targeting diversification in small portfolios. The strategy employs k-means cluster analysis to eliminate the cardinality constraint and thereby simplify the mathematical model and the evolutionary optimization process. An evolution strategy which is a variant of the conventional ( mu+ lambda) evolution strategy but employs real coded genes with genetic inheritance operators such as arithmetic variable point cross over and real number uniform mutation to initiate a fast converging reproduction process has been evolved to solve the simplified model. The strategy also employs refined weight standardization algorithms to tackle the bounding and class constraints. Experimental results have been demonstrated on the Bombay Stock Exchange, India (BSE200 index, Period: July 2001-July 2006) and on the Tokyo Stock Exchange, Japan (Nikkei225 index, Period: March 2002-March 2007) datasets and compared with those obtained by the Markowitz mean-variance, random matrix theory filtered, and quadratic programming-based solution models for the appropriate cases.

Stream cipher for binary image encryption using Ant Colony Optimization based key generation

Abstract: Encryption of binary images is essential since it is vulnerable to eavesdropping in wired and wireless networks. The security of data becomes important since the communications over open network occur frequently. This paper focuses on encryption of binary image using a stream cipher method. In this paper we propose an Ant Colony Optimization (ACO) based approach of generating keys for encryption. The binary image is represented in a text form and encrypted using a stream cipher method. A novel technique termed Ant Colony Optimization Key Generation Binary Image Encryption (AKGBE) algorithm employs a character code table for encoding the keys and the plain text representing the binary image. The main advantage of this approach is that it reduces the number of keys to be stored and distributed. Experimental results demonstrating AKGBE's encrypting binary images of different sizes and the comparison of its performance with other stream cipher methods are presented.

Image recognition using simplified fuzzy artmap augmented with a moment based feature extractor

Abstract: The capability of Kasuba's Simplified Fuzzy ARTMAP (SFAM) to behave as a Pattern Recognizer/Classifier of images both noisy and noise free has been investigated in this paper. This calls for augmenting the original Neuro–Fuzzy model with a modified moment-based RST invariant feature extractor. The potential of the SFAM based Pattern Recognizer to recognize patterns — monochrome and color, noisy and noise free — has been studied on two experimental problems. The first experiment which concerns monochrome images, pertains to recognition of satellite images, a problem discussed by Wang et al. The second experiment, which concerns color images, deals with the recognition of some sample test colored patterns. The results of the computer simulation have also been presented.

Fuzzy Decision Theory Based Metaheuristic Portfolio Optimization and Active Rebalancing Using Interval Type-2 Fuzzy Sets

Abstract: This paper discusses a hitherto unexplored problem of fuzzy portfolio optimization with its tripartite stages of portfolio optimization, market scenario forecasting, and portfolio rebalancing. The portfolio optimization phase, which determines the original portfolio to be invested in, deals with the multiobjectives of maximizing its diversification ratio and its expected portfolio return, subject to the nonlinear constraints of risk budgeting and other investor preferential constraints. The complex problem model necessarily depends on metaheuristics to arrive at the optimal portfolio. The market scenario forecasting phase, where the investor desires to generate future market scenarios to handle the uncertainty in the markets while attempting to rebalance the portfolio, adopts a strategically refined Monte Carlo simulation to generate close-to-real future market scenarios. The last stage of portfolio rebalancing, which is crucial to the investor, employs fuzzy decision theory based metaheuristics using Interval Type-2 fuzzy sets, to cleverly exploit the uncertainty modeled by the market scenarios generated, to arrive at the ultimate optimal rebalanced portfolio. In the absence of reported work for a complex problem of such a nature and scale, two metaheuristic strategies chosen from two different genres of evolutionary algorithms, viz., multiobjective differential evolution and multiobjective evolution strategy, have been strategically evolved to solve the multistaged problem, for comparison of results. The experimental studies have been undertaken over a high-risk portfolio of BSE 200 index (March 1999–March 2009, Bombay Stock Exchange, India). Extensive simulations including data envelopment analysis have been undertaken to analyze the performance and robustness of the solution strategies.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Moea/d with adaptive weight adjustment

Abstract: Recently, MOEA/D multi-objective evolutionary algorithm based on decomposition has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem MOP into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front PF; i.e., discontinuous PF or PF with sharp peak or low tail. To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment MOEA/D-AWA. According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, -MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.