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 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.

Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

2021 ◽  
pp. 1074-1115
Author(s):  
Victer Paul ◽  
Ganeshkumar C ◽  
Jayakumar L
Keyword(s):  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Computation Time ◽  
Search Space ◽  
Population Based ◽  
Vital Role ◽  
Traveling Salesman ◽  
Performance Criteria

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

2019 ◽  
Vol 10 (2) ◽  
pp. 55-92 ◽  
Author(s):  
Victer Paul ◽  
Ganeshkumar C ◽  
Jayakumar L
Keyword(s):  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Computation Time ◽  
Search Space ◽  
Population Based ◽  
Vital Role ◽  
Traveling Salesman ◽  
Performance Criteria

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

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.

scholarly journals Enhancing genetic algorithms using multi mutations

2016 ◽  
Author(s):  
Ahmad B Hassanat ◽  
Esra’a Alkafaween ◽  
Nedal A Alnawaiseh ◽  
Mohammad A Abbadi ◽  
Mouhammd Alkasassbeh ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Novel Mutation ◽  
Mutation Operator ◽  
Trial And Error ◽  
Travelling Salesman ◽  
Selection Strategies ◽  
Mutation Operators ◽  
Global Optima ◽  
More Trial ◽  
Selection Of

Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error. This paper investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. Novel mutation operators are proposed, in addition to two selection strategies for the mutation operators, one of which is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) were conducted to evaluate the proposed methods, and these were compared to the well-known exchange mutation and rearrangement mutation. The results show the importance of some of the proposed methods, in addition to the significant enhancement of the genetic algorithm’s performance, particularly when using more than one mutation operator.

scholarly journals Global Optimization of an Analog Method by Means of Genetic Algorithms

2017 ◽  
Vol 145 (4) ◽  
pp. 1275-1294 ◽  
Author(s):  
Pascal Horton ◽  
Michel Jaboyedoff ◽  
Charles Obled
Keyword(s):  
Genetic Algorithms ◽  
Global Optimization ◽  
Degrees Of Freedom ◽  
Optimization Technique ◽  
Global Optimum ◽  
Optimization Approach ◽  
Local Weather ◽  
Temporal Windows ◽  
Global Optimization Technique ◽  
Selection Of

Abstract Analog methods are based on a statistical relationship between synoptic meteorological variables (predictors) and local weather (predictand, to be predicted). This relationship is defined by several parameters, which are often calibrated by means of a semiautomatic sequential procedure. This calibration approach is fast, but has strong limitations. It proceeds through successive steps, and thus cannot handle all parameter dependencies. Furthermore, it cannot automatically optimize some parameters, such as the selection of pressure levels and temporal windows (hours of the day) at which the predictors are compared. To overcome these limitations, the global optimization technique of genetic algorithms is considered, which can jointly optimize all parameters of the method, and get closer to a global optimum, by taking into account the dependencies of the parameters. Moreover, it can objectively calibrate parameters that were previously assessed manually and can take into account new degrees of freedom. However, genetic algorithms must be tailored to the problem under consideration. Multiple combinations of algorithms were assessed, and new algorithms were developed (e.g., the chromosome of adaptive search radius, which is found to be very robust), in order to provide recommendations regarding the use of genetic algorithms for optimizing several variants of analog methods. A global optimization approach provides new perspectives for the improvement of analog methods, and for their application to new regions or new predictands.

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.

Particle Swarm Optimization With Crossover and Mutation Operators Using the Diversity Criteria

2013 ◽  
Author(s):  
Guang Dong ◽  
John Cooper
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Population Diversity ◽  
L1 Norm ◽  
Swarm Optimization ◽  
Mutation Operators ◽  
Crossover And Mutation

Particle Swarm Optimization is a population based globalized search algorithm that mimics the behavior of swarms. It belongs to the larger class of evolutionary algorithms as widely used stochastic technique in the global optimization field. Since the PSO is population based, it requires no auxiliary information, such as the gradient of the problem. It is known that each particle in the PSO uses only two pieces of information, called the personal best position and the global best position, to update its moving velocity and position by generations. One disadvantage of this algorithm is that it can be easily trapped into some local optimal solutions because of the premature convergence. This may be an issue when solving complex multi-modal functions with multiple local minimums. Hence, the global optimization algorithm should have the ability to prevent being trapped into local optima by keeping wide search space and maintaining the population diversity. In order to improve the performance of the PSO for complex global optimization problems, this paper introduces both crossover and mutation operators to the basic PSO algorithm. The proposed algorithm uses the mechanism that all the particles in the current iteration will have crossover and mutation operations if the diversity criteria of the particles is reduced to be smaller than a predefined limit value. Therefore, the PSO using both crossover and mutation operators can maintain the diversity of population and enhance the search ability as to get better results while solving complex problems. This study adopts the average distance around the swarm center as the diversity measure, and extends the distance metrics to both L1 norm distance and L∞ norm distance. To verify the usability and effectiveness of the proposed algorithm, it is applied to 12 widely used nonlinear benchmark functions. These examples show that the proposed PSO with crossover and mutation operators using the diversity criteria has better optimization performance than the basic PSO by maintaining the swarm diversity. Moreover, the PSO using the L1 norm distance diversity gives better results than both L2 and L∞ norm distance for most cases.

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