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 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.

ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM

2009 ◽  
Vol 05 (02) ◽  
pp. 487-496 ◽  
Author(s):  
WEI FANG ◽  
JUN SUN ◽  
WENBO XU
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Novel Mutation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mutation Operator ◽  
Fundamental Theory ◽  
Swarm Optimization ◽  
Mutation Operators ◽  
Better Than

Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.

scholarly journals COMPARATIVE STUDY OF MUTATION OPERATORS IN THE GENETIC ALGORITHMS FOR THE K-MEANS PROBLEM

2021 ◽  
pp. 1091
Author(s):  
Rui Li ◽  
Lev A. Kazakovtsev
Keyword(s):  
Genetic Algorithms ◽  
Clustering Algorithm ◽  
Optimization Method ◽  
Neighborhood Search ◽  
Mutation Operator ◽  
Data Set ◽  
Comparative Efficiency ◽  
Mutation Operators ◽  
Clustering Model ◽  
Genetic Clustering

The k-means problem and the algorithm of the same name are the most commonly used clustering model and algorithm. Being a local search optimization method, the k-means algorithm falls to a local minimum of the objective function (sum of squared errors) and depends on the initial solution which is given or selected randomly. This disadvantage of the algorithm can be avoided by combining this algorithm with more sophisticated methods such as the Variable Neighborhood Search, agglomerative or dissociative heuristic approaches, the genetic algorithms, etc. Aiming at the shortcomings of the k-means algorithm and combining the advantages of the k-means algorithm and rvolutionary approack, a genetic clustering algorithm with the cross-mutation operator was designed. The efficiency of the genetic algorithms with the tournament selection, one-point crossover and various mutation operators (without any mutation operator, with the uniform mutation, DBM mutation and new cross-mutation) are compared on the data sets up to 2 millions of data vectors. We used data from the UCI repository and special data set collected during the testing of the highly reliable semiconductor components. In this paper, we do not discuss the comparative efficiency of the genetic algorithms for the k-means problem in comparison with the other (non-genetic) algorithms as well as the comparative adequacy of the k-means clustering model. Here, we focus on the influence of various mutation operators on the efficiency of the genetic algorithms only.

scholarly journals Fitness-Orientated Mutation Operators in Genetic Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 1769-1772
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Controlled Experiment ◽  
The Other ◽  
Mutation Operator ◽  
Natural Evolution ◽  
Mutation Operators ◽  
Positive Method ◽  
Crossover And Mutation ◽  
Random Approach

Genetic Algorithms are the most popular metaheuristics used in search-based program solving. They search for a solution by repeating the following operators: selection, crossover, and mutation. By going through several generations of these operators, a solution is reached. The total number of generations depends on the reproduction of offspring. Of the reproduction operators, the mutation operator tends to be chosen with a random approach because the concept of mutation is from the natural evolution cycle. But the effectiveness of genetic algorithms should be monitored, especially in software testing. The effectiveness is represented by the total number of generations, which corresponds to the speed of solution acquisition. This work focuses on mutation as a factor in reducing the total number of generations and devises two contrasting ways to define mutation operators. One is fitness-positive, and the other is fitness-negative. The fitness-negative definition thus appears to fit more aptly. This work determines which of these two methods of mutation achieves the higher effectiveness through conducting a controlled experiment. The result shows that the fitness-positive method takes a smaller number of generations than the fitness-negative method.

scholarly journals K-Means Genetic Algorithms with Greedy Genetic Operators

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Lev Kazakovtsev ◽  
Ivan Rozhnov ◽  
Guzel Shkaberina ◽  
Viktor Orlov
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Computation Time ◽  
Good Choice ◽  
Population Diversity ◽  
Greedy Heuristic ◽  
Mutation Operator ◽  
Crossover Operator ◽  
Genetic Operators ◽  
Mutation Operators

The k-means problem is one of the most popular models of cluster analysis. The problem is NP-hard, and modern literature offers many competing heuristic approaches. Sometimes practical problems require obtaining such a result (albeit notExact), within the framework of the k-means model, which would be difficult to improve by known methods without a significant increase in the computation time or computational resources. In such cases, genetic algorithms with greedy agglomerative heuristic crossover operator might be a good choice. However, their computational complexity makes it difficult to use them for large-scale problems. The crossover operator which includes the k-means procedure, taking the absolute majority of the computation time, is essential for such algorithms, and other genetic operators such as mutation are usually eliminated or simplified. The importance of maintaining the population diversity, in particular, with the use of a mutation operator, is more significant with an increase in the data volume and available computing resources such as graphical processing units (GPUs). In this article, we propose a new greedy heuristic mutation operator for such algorithms and investigate the influence of new and well-known mutation operators on the objective function value achieved by the genetic algorithms for large-scale k-means problems. Our computational experiments demonstrate the ability of the new mutation operator, as well as the mechanism for organizing subpopulations, to improve the result of the algorithm.

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 Asymptotic Expansions and Strategies in the Online Increasing Subsequence Problem

2021 ◽  
Vol 174 (1) ◽  
Author(s):  
Amirlan Seksenbayev
Keyword(s):  
Dynamic Programming ◽  
Asymptotic Expansions ◽  
Dual Problem ◽  
Infinite Length ◽  
Expected Time ◽  
Selection Strategies ◽  
Dynamic Programming Equation ◽  
Optimal Values ◽  
Design Selection ◽  
Selection Of

AbstractWe study two closely related problems in the online selection of increasing subsequence. In the first problem, introduced by Samuels and Steele (Ann. Probab. 9(6):937–947, 1981), the objective is to maximise the length of a subsequence selected by a nonanticipating strategy from a random sample of given size $n$ n . In the dual problem, recently studied by Arlotto et al. (Random Struct. Algorithms 49:235–252, 2016), the objective is to minimise the expected time needed to choose an increasing subsequence of given length $k$ k from a sequence of infinite length. Developing a method based on the monotonicity of the dynamic programming equation, we derive the two-term asymptotic expansions for the optimal values, with $O(1)$ O ( 1 ) remainder in the first problem and $O(k)$ O ( k ) in the second. Settling a conjecture in Arlotto et al. (Random Struct. Algorithms 52:41–53, 2018), we also design selection strategies to achieve optimality within these bounds, that are, in a sense, best possible.

scholarly journals Adaptive Gene Level Mutation

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Jalal Al-afandi ◽  
Horváth András
Keyword(s):  
Genetic Algorithms ◽  
Stochastic Optimization ◽  
Specific Problem ◽  
Traditional Approach ◽  
Fitness Function ◽  
Optimization Methods ◽  
Analytic Solutions ◽  
Problem Representation ◽  
Travelling Salesman ◽  
Gene Level

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

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