Rajashree Mishra

Chemo-inspired genetic algorithm for function optimization

Finding global optimal solution for a non-linear optimization problem with high complexity became a challenge for the researchers. Evolutionary optimization process is being treated as an alternate paradigm to solve such problems. In this context, Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from foraging behavior of Escherichia coli bacterium. At the same time, Genetic Algorithm (GA) has also achieved popularity from the academic and industrial communities to deal with such problems. To improve the solution quality further, the hybridization of GA with BFO (GA-BF) is proven to be much robust in recent past. It is found that while hybridizing GA with BFO, the behavior/mechanism of some of the operators seems to be repeated and it may lead to hamper the solution quality as well as increases the computational time. Hence, instead of taking the whole BFO to hybridize with GA (called GA-BF); only the chemotaxis step is picked from BFO mechanism and hybridized with GA. It is named as chemo-inspired Genetic Algorithm (CGA). The superiority of CGA over GA-BF algorithm is being realized through a set of four typical benchmark problems available in the literature. Later, the faster convergence of CGA is shown graphically. Hence, CGA outperforms GA-BF in terms of solution quality and the computational time. Moreover, two real life problems namely (a) solving system of linear equations and (b) frequency modulation sounds parameter identification problems have been solved, in order to justify the above conclusion.

Fuzzy Stochastic Genetic Algorithm for Obtaining Optimum Crops Pattern and Water Balance in a Farm

This paper is concerned with multi-objective fuzzy stochastic model for determination of optimum cropping patterns with water balance for the next crop season. The objective functions of the model is to study the effect of various cropping patterns on crop production subject to total water supply in a small farm. The decision variables are the cultivated area of different crops at the farm. The water requirement of the crops follows fuzzy uniform distribution and yields in the objective functions are taken as a fuzzy numbers. The model is solved by using fuzzy stochastic simulation based genetic algorithm without deriving the deterministic equivalents.

A Performance Study of Chemo-Inspired Genetic Algorithm on Benchmark Functions

In solving non-linear optimization problems, Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from foraging behavior of E. Coli bacterium. In the other hand, Genetic algorithm (GA) has attracted increased attention from the academic and industrial communities to deal with such problems. In recent literature, it is discovered that the hybrid techniques provides the better solution with faster convergence. In this paper, a novel approach of hybridization is presented. The Chemotactic step (from BFO) is only hybridized with GA, namely CGA. The better performance of the proposed CGA than Quadratic Approximation hybridized GA, is experimentally verified through a set of 22 benchmark problems taken from recent literature.

Chemo-inspired Genetic Algorithm and Application to Model Order Reduction Problem

During the past three decades, evolutionary computing techniques have grown manifold in tackling all sorts of optimization problems. Genetic algorithm (GA) is one of the most popular EAs because it is easy to implement and is conducive for noisy environment. Similarly, amongst several swarm intelligence techniques, bacterial foraging optimization (BFO) is the recent popular algorithm being used for many practical applications. Depending on the complexity of the problem concerned, there is need for hybridized techniques which help in balancing exploration and exploitation capability over the search space. Many hybridized techniques have been developed recently to tackle such problems. This paper proposes a hybridization of GA and BFO to solve a real-life unconstrained electrical engineering problem. This unconstrained optimization problem is a model order reduction (MOR) problem of linear time invariant continuous single input and single output (SISO) system.

Solving Sudoku Puzzles Using Evolutionary Techniques—A Systematic Survey

Sudoku puzzle is a game which takes the form of an N × N matrix. It requires the players to organize the number sequences from 1 to N in the submatrices of the original matrix in such a way that no numbers are reused in each sub matrices and also the numbers are not reused in each column and rows. It is mainly based on the number replacement game and is a combinatorial puzzle. Several evolutionary techniques such as Genetic algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Artificial Bee Colony Optimization are used for solving, rating, and generating Sudoku Puzzles. This research paper presents a survey of solving Sudoku Puzzles using different evolutionary technique-based hybridized algorithms and analyze the results, i.e., success rates found in solving the puzzles of different levels such as Easy, Medium, Challenging, Hard, Evil, and Super Hard.

A Systematic Review of Software Testing Using Evolutionary Techniques

A best solution for decreasing software cost and reducing the cycle time during software development is automatic software testing and it has been seen by various organization. User specifications and requirements can be fully achieved by software testing. A number of issues are underlying in the field of software testing such as prioritization of test cases and automatic and effective test case generation are to be handled properly and they mostly depends on duration, cost and effort during the testing process. Testing can be done in two different ways such as manual testing and automatic testing by using different testing tools. Manual testing are very time consuming and this can be overcome by automatic testing by generating test cases automatically. Several types of evolutionary techniques like Genetic Algorithm, Particle Swarm Optimization and Bee Colony Optimization have been used for software testing. In this research paper, a survey of different evolutionary techniques used in software testing have been presented by taking the various issues in to account.

A Novel Chemo-inspired Genetic Algorithm for Economic Load Dispatch with Valve Point Loading Effect

The inherent drawback of the popular evolutionary algorithm as such genetic algorithm (GA) and also bio-inspired algorithm bacterial foraging optimization (BFO) lies in the fact that they very often suffer from the problem of being trapped into the local optimum. In recent past, various popular hybridized techniques of GA and BFO came out through different thought processes of the researches and have been implemented in the algorithm. Inspired by those ideas, in this paper, a novel approach has been opted for the hybridization of GA with BFO by incorporating chemotactic step as a local search operator at the end of the entire GA cycle; thus, the algorithm is named as chemo-inspired genetic algorithm (CGA) and it has also been extended for constrained optimization, and further it is named as CGAC, where “C” stands for being capable of handling constraints. At the outset, experiments are made to validate the superiority of CGAC over another hybrid method, namely LX-PM-C and H-LX-PM-C taking a set of 8 typical benchmark problems of various difficulty labels from the literature. Later, it has been applied to real-life application problem, where economic load dispatch (ELD) problem having 40 generators has been considered with valve point loading effect.

Test Case Optimization and Prioritization Based on Multi-objective Genetic Algorithm

The validation of modified software depends on the success of Regression testing. For this, test cases are selected in such a way that can detect a maximum number of faults at the earliest stage of software development. The selection process in which the most beneficial test case are executed first is known as test case prioritization which improves the performance of execution of test cases in a specific or appropriate order. Many optimizing techniques like greedy algorithm, genetic algorithm, and metaheuristic search techniques have been used by many researchers for test case prioritization and optimization. This research paper presents a test case prioritization and optimization method using genetic algorithm by taking different factors of test cases like statement coverage data, requirements factors, risk exposure, and execution time.

Fuzzy stochastic price scenario based portfolio selection and its application to BSE using genetic algorithm

Abstract This paper is concerned with portfolio selection problem using a fuzzy stochastic price scenario. In this scenario, a ratio factor (k) is calculated from the historical data to generated the future price of the stocks of Bombay Stock Exchange. The ratio factor k of different stocks are treated as a fuzzy numbers, which in turn gives future fuzzy prices of the stocks. Returns on the stocks are calculated from the future price of the stocks. Rejection of the assets are done based on returns calculated from the worst case scenario. If the returns of an asset exceed the investors risk tolerance then the asset are not included in the portfolio. The definition of capital budget has been reformed to include the transaction cost with the capital budget. This process is implemented in two stage multi-objective fuzzy probabilistic programming problem which is then solved using a fuzzy genetic algorithm to obtain maximum short term and long term returns. A case study of Bombay Stock Exchange is provided to illustrate the above model.

Genetic algorithm approach for solving multi-objective fuzzy stochastic programming problem

This paper is concerned with the solution procedure of a multi-objective fuzzy stochastic optimisation problem by simulation-based genetic algorithm. In this article, a multi-objective fuzzy chance constrained programming problem is considered with continuous fuzzy random variables. The uncertain parameters are considered as fuzzy normal and fuzzy log-normal random variables. The feasibilities of the fuzzy chance constraints are checked by the fuzzy stochastic programming with the genetic process without deriving the deterministic equivalents. The proposed procedure is illustrated by a numerical example.