scholarly journals Software Systems Clustering Using Estimation of Distribution Approach

2016 ◽  
Vol 8 (2) ◽  
pp. 99-113 ◽  
Author(s):  
Mahjoubeh Tajgardan ◽  
Habib Izadkhah ◽  
Shahriar Lotfi
Keyword(s):  
Genetic Algorithms ◽  
Probabilistic Models ◽  
Evolutionary Process ◽  
Solution Space ◽  
Building Blocks ◽  
Genetic Operators ◽  
Software Clustering ◽  
Mutation Operators ◽  
Estimation Of Distribution ◽  
Crossover And Mutation

AbstractSoftware clustering is usually used for program understanding. Since the software clustering is a NP-complete problem, a number of Genetic Algorithms (GAs) are proposed for solving this problem. In literature, there are two wellknown GAs for software clustering, namely, Bunch and DAGC, that use the genetic operators such as crossover and mutation to better search the solution space and generating better solutions during genetic algorithm evolutionary process. The major drawbacks of these operators are (1) the difficulty of defining operators, (2) the difficulty of determining the probability rate of these operators, and (3) do not guarantee to maintain building blocks. Estimation of Distribution (EDA) based approaches, by removing crossover and mutation operators and maintaining building blocks, can be used to solve the problems of genetic algorithms. This approach creates the probabilistic models from individuals to generate new population during evolutionary process, aiming to achieve more success in solving the problems. The aim of this paper is to recast EDA for software clustering problems, which can overcome the existing genetic operators’ limitations. For achieving this aim, we propose a new distribution probability function and a new EDA based algorithm for software clustering. To the best knowledge of the authors, EDA has not been investigated to solve the software clustering problem. The proposed EDA has been compared with two well-known genetic algorithms on twelve benchmarks. Experimental results show that the proposed approach provides more accurate results, improves the speed of convergence and provides better stability when compared against existing genetic algorithms such as Bunch and DAGC.

Optimization of Genetic Operators for Scheduling Problems

2007 ◽  
Vol 11 (9) ◽  
pp. 1092-1098 ◽  
Author(s):  
António Ferrolho ◽  
◽  
Manuel Crisóstomo ◽  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Job Scheduling ◽  
Software Tool ◽  
Scheduling Problems ◽  
Genetic Operators ◽  
Mutation Operators ◽  
Crossover And Mutation

Genetic algorithms (GA) can provide good solutions for scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. This paper presents and examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems.

scholarly journals Study on Selection Methods of Parents and Crossover in Genetic Algorithm

2021 ◽  
pp. 280-285
Author(s):  
Dr. K. Santhi ◽  
Dr. V. Vinodhini
Keyword(s):  
Genetic Algorithms ◽  
Optimization Technique ◽  
Population Based ◽  
Complex Problem ◽  
Genetic Operators ◽  
Mutation Operators ◽  
The People ◽  
Population Based Search ◽  
Crossover And Mutation ◽  
Selection Of

Genetic Algorithms are the population based search and optimization technique that mimic the process of Genetic and Natural Evolution. Genetic algorithms are very effective way of finding an Optimized solution to a complex problem. Performance of genetic algorithms mainly depends on various factors such as selection of efficient parents and type of genetic operators which involve crossover and mutation operators etc. This paper will help the people to acquire the knowledge about various strategies of selecting parents and description about standard crossover operators.

scholarly journals Information Theoretic Objective Function for Genetic Software Clustering

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 18
Author(s):  
Habib Izadkhah ◽  
Mahjoubeh Tajgardan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Objective Function ◽  
High Performance ◽  
Evolutionary Process ◽  
Program Comprehension ◽  
Software Systems ◽  
Objective Functions ◽  
Information Theoretic ◽  
Software Clustering

Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions’ limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

Decomposition of Black-Box Optimization Problems by Community Detection in Bayesian Networks

2012 ◽  
Vol 3 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Marcio K. Crocomo ◽  
Jean P. Martins ◽  
Alexandre C. B. Delbem
Keyword(s):  
Probabilistic Models ◽  
Optimization Problems ◽  
Black Box ◽  
Point Of View ◽  
Bayesian Optimization ◽  
Genetic Operators ◽  
Step Method ◽  
Bayesian Optimization Algorithm ◽  
Probabilistic Sampling ◽  
Estimation Of Distribution

Estimation of Distribution Algorithms (EDAs) have proved themselves as an efficient alternative to Genetic Algorithms when solving nearly decomposable optimization problems. In general, EDAs substitute genetic operators by probabilistic sampling, enabling a better use of the information provided by the population and, consequently, a more efficient search. In this paper the authors exploit EDAs' probabilistic models from a different point-of-view, the authors argue that by looking for substructures in the probabilistic models it is possible to decompose a black-box optimization problem and solve it in a more straightforward way. Relying on the Building-Block hypothesis and the nearly-decomposability concept, their decompositional approach is implemented by a two-step method: 1) the current population is modeled by a Bayesian network, which is further decomposed into substructures (communities) using a version of the Fast Newman Algorithm. 2) Since the identified communities can be seen as sub-problems, they are solved separately and used to compose a solution for the original problem. The experiments showed strengths and limitations for the proposed method, but for some of the tested scenarios the authors’ method outperformed the Bayesian Optimization Algorithm by requiring up to 78% fewer fitness evaluations and being 30 times faster.

Linkage Identification by Fitness Difference Clustering

2006 ◽  
Vol 14 (4) ◽  
pp. 383-409 ◽  
Author(s):  
Miwako Tsuji ◽  
Masaharu Munetomo ◽  
Kiyoshi Akama
Keyword(s):  
Genetic Algorithms ◽  
Perturbation Methods ◽  
Computational Cost ◽  
Building Blocks ◽  
Estimation Of Distribution Algorithms ◽  
Novel Approach ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Class Of Functions ◽  
Dependency Detection

Genetic Algorithms perform crossovers effectively when linkage sets — sets of variables tightly linked to form building blocks — are identified. Several methods have been proposed to detect the linkage sets. Perturbation methods (PMs) investigate fitness differences by perturbations of gene values and Estimation of distribution algorithms (EDAs) estimate the distribution of promising strings. In this paper, we propose a novel approach combining both of them, which detects dependencies of variables by estimating the distribution of strings clustered according to fitness differences. The proposed algorithm, called the Dependency Detection for Distribution Derived from fitness Differences (D5), can detect dependencies of a class of functions that are difficult for EDAs, and requires less computational cost than PMs.

scholarly journals Structural Topology Optimization in Linear and Nonlinear Elasticity Using Genetic Algorithms

1995 ◽  
Author(s):  
Couro Kane ◽  
François Jouve ◽  
Marc Schoenauer
Keyword(s):  
Genetic Algorithms ◽  
Topology Optimization ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Optimal Solutions ◽  
Mutation Operators ◽  
Geometrical Effects ◽  
Displacement Constraints ◽  
Crossover And Mutation ◽  
Linear And Nonlinear Elasticity

Abstract In this paper, structural topology optimization is addressed through Genetic Algorithms. A set of designs is evolved following the Darwinian survival-of-fittest principle. The standard crossover and mutation operators are tailored for the needs of 2D topology optimization. The genetic algorithm based on these operators is experimented on plane stress problems of cantilever plates: the goal is to optimize the weight of the structure under displacement constraints. The main advantage of this approach is that it can both find out alternative optimal solutions, as experimentally demonstrated on a problem with multiple solutions, and handle different kinds of mechanical model: some results in elasticity with large displacements are presented. In that case, the nonlinear geometrical effects of the model lead to non viable solutions, unless some constraints are imposed on the stress field.

Improved Genetic Algorithms for Software Testing Cases Generation

2013 ◽  
Vol 380-384 ◽  
pp. 1464-1468
Author(s):  
Shun Kun Yang ◽  
Fu Ping Zeng
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Fitness Function ◽  
Automatic Generation ◽  
Improved Genetic Algorithm ◽  
Dynamic Adjustment ◽  
Genetic Operators ◽  
Adaptive Genetic Algorithms ◽  
Crossover And Mutation ◽  
Testing Coverage

In order to realize the adaptive Genetic Algorithms to balance the contradiction between algorithm convergence rate and algorithm accuracy for automatic generation of software testing cases, improved Genetic Algorithms is proposed for different aspects. Orthogonal method and Equivalence partitioning are employed together to make the initial testing population more effective with more reasonable coverage; Genetic operators of Crossover and Mutation is defined adaptively by the dynamic adjustment according to multi-objective Fitness function, which can guide the testing process more properly and realize the biggest testing coverage to find more defects as far as possible. Finally, the improved Genetic Algorithm are compared and analyzed by testing one benchmark program to verify its feasibility and effectiveness.

scholarly journals Tuning Genetic Algorithm Parameters to Improve Convergence Time

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Maria Angelova ◽  
Tania Pencheva
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Method ◽  
Convergence Time ◽  
Generation Gap ◽  
Genetic Operators ◽  
Time Saving ◽  
Fermentation Processes ◽  
Highly Nonlinear ◽  
Crossover And Mutation

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation ofS. cerevisiaealtogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.

Integrated Process Planning and Scheduling Based on Genetic Algorithms

2011 ◽  
Vol 291-294 ◽  
pp. 331-334
Author(s):  
Jin Feng Wang ◽  
Shi Jie Li ◽  
Shun Cheng Fan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Process Planning ◽  
Manufacturing System ◽  
Simulation Studies ◽  
Integrated Process ◽  
Improved Genetic Algorithm ◽  
Planning And Scheduling ◽  
Mutation Operators ◽  
Crossover And Mutation

Process planning and scheduling are two important manufacturing activities in the manufacturing system. In this paper, an improved genetic algorithm(GA) has been developed to facilitate the integration and optimization of process planning and scheduling. To improve the optimization performance, an efficient genetic representation has been developed. Selection, crossover, and mutation operators have been described. Simulation studies have been established to evaluate the performance of the algorithm. The results show that the algorithm is a promising and effective method for the integration of process planning and scheduling(IPPS).

A Comparison of the Fixed and Floating Building Block Representation in the Genetic Algorithm

1996 ◽  
Vol 4 (2) ◽  
pp. 169-193 ◽  
Author(s):  
Annie S. Wu ◽  
Robert K. Lindsay
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Building Block ◽  
Deoxyribonucleic Acid ◽  
Evolutionary Process ◽  
Building Blocks ◽  
Problem Representation ◽  
Parallel Search ◽  
Diverse Population ◽  
Significant Difference

This article compares the traditional, fixed problem representation style of a genetic algorithm (GA) with a new floating representation in which the building blocks of a problem are not fixed at specific locations on the individuals of the population. In addition, the effects of noncoding segments on both of these representations is studied. Noncoding segments are a computational model of noncoding deoxyribonucleic acid, and floating building blocks mimic the location independence of genes. The fact that these structures are prevalent in natural genetic systems suggests that they may provide some advantages to the evolutionary process. Our results show that there is a significant difference in how GAs solve a problem in the fixed and floating representations. Genetic algorithms are able to maintain a more diverse population with the floating representation. The combination of noncoding segments and floating building blocks appears to encourage a GA to take advantage of its parallel search and recombination abilities.

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