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 A Novel Parent Centric Crossover with the Log-Logistic Probabilistic Approach Using Multimodal Test Problems for Real-Coded Genetic Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Ehtasham ul Haq ◽  
Ishfaq Ahmad ◽  
Ibrahim M. Almanjahie
Keyword(s):  
Optimization Problems ◽  
Probabilistic Approach ◽  
Population Diversity ◽  
Test Problems ◽  
Crossover Operator ◽  
Local Optima ◽  
Mutation Operators ◽  
Multiple Comparison Test ◽  
Power Mutation ◽  
Real Coded Genetic Algorithms

In this paper, a comprehensive empirical study is conducted to evaluate the performance of a new real-coded crossover operator called Fisk crossover (FX) operator. The basic aim of the proposed study is to preserve population diversity as well as to avoid local optima. In this context, a new crossover operator is designed and developed which is linked with Log-logistic probability distribution. For its global performance, a realistic comparison is made between FX versus double Pareto crossover (DPX), Laplace crossover (LX), and simulated binary crossover (SBX) operators. Moreover, these crossover operators are also used in conjunction with three mutation operators called power mutation (PM), Makinen, Periaux, and Toivanen mutation (MPTM), and nonuniform mutation (NUM) for inclusive evaluation. The performance of probabilistic-based algorithms is tested on a set of twenty-one well-known nonlinear optimization benchmark functions with diverse features. The empirical results show a substantial dominance of FX over other crossover operators with authentication of performance index (PI). Moreover, we also examined the significance of the proposed crossover scheme by administrating ANOVA and Gabriel pairwise multiple comparison test. Finally, the statistically significant results of the proposed crossover scheme have a definite edge over the other schemes, and it is also expected that FX has a great potential to solve complex optimization problems.

Self-Adaptation of Mutation Operator and Probability for Permutation Representations in Genetic Algorithms

2010 ◽  
Vol 18 (3) ◽  
pp. 491-514 ◽  
Author(s):  
Martin Serpell ◽  
James E. Smith
Keyword(s):  
Mutation Rate ◽  
Poor Performance ◽  
Adaptive Methods ◽  
Optimal Choice ◽  
Problem Instance ◽  
Mutation Operator ◽  
Genetic Operators ◽  
Mutation Operators ◽  
Wide Range ◽  
Self Adaptation

The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is no one “optimal choice”; rather, the situation changes per problem instance and during evolution. This paper examines whether this choice can be left to the processes of evolution via self-adaptation, thus removing this nontrivial task from the GA user and reducing the risk of poor performance arising from (inadvertent) inappropriate decisions. Self-adaptation has been proven successful for mutation step sizes in the continuous domain, and for the probability of applying bitwise mutation to binary encodings; here we examine whether this can translate to the choice and parameterisation of mutation operators for permutation encodings. We examine one method for adapting the choice of operator during runtime, and several different methods for adapting the rate at which the chosen operator is applied. In order to evaluate these algorithms, we have used a range of benchmark TSP problems. Of course this paper is not intended to present a state of the art in TSP solvers; rather, we use this well known problem as typical of many that require a permutation encoding, where our results indicate that self-adaptation can prove beneficial. The results show that GAs using appropriate methods to self-adapt their mutation operator and mutation rate find solutions of comparable or lower cost than algorithms with “static” operators, even when the latter have been extensively pretuned. Although the adaptive GAs tend to need longer to run, we show that is a price well worth paying as the time spent finding the optimal mutation operator and rate for the nonadaptive versions can be considerable. Finally, we evaluate the sensitivity of the self-adaptive methods to changes in the implementation, and to the choice of other genetic operators and population models. The results show that the methods presented are robust, in the sense that the performance benefits can be obtained in a wide range of host algorithms.

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 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 Machine-coded genetic operators and their performances in floating-point genetic algorithms

2017 ◽  
Vol 5 (1) ◽  
pp. 8 ◽  
Author(s):  
Mehmet Hakan Satman ◽  
Emre Akadal
Keyword(s):  
Genetic Algorithms ◽  
Search Space ◽  
Fine Tuning ◽  
Floating Point ◽  
Mutation Operator ◽  
Genetic Operators ◽  
Random Mutation ◽  
Genetic Search ◽  
Decision Variables ◽  
New Type

Machine-coded genetic algorithms (MCGAs) use the byte representation of floating-point numbers which are encoded in the computer memory. Use of the byte alphabet makes classical crossover operators directly applicable in the floating-point genetic algorithms. Since effect of the byte-based mutation operator depends on the location of the mutated byte, the byte-based mutation operator mimics the functionality of its binary counterpart. In this paper, we extend the MCGA by developing new type of byte-based genetic operators including a random mutation and a random dynamic mutation operator. We perform a simulation study to compare the performances of the byte-based operators with the classical FPGA operators using a set of test functions. The prepared software package, which is freely available for downloading, is used for the simulations. It is shown that the byte-based genetic search obtains precise results by carrying out the both exploration and exploitation tasks by discovering new fields of the search space and performing a local fine-tuning. It is also shown that the introduced byte-based operators improve the search capabilities of FPGAs by means of convergence rate and precision even if the decision variables are in larger domains.

scholarly journals Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

2020 ◽  
Vol 10 (20) ◽  
pp. 7264
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Multicast Routing ◽  
Test Case ◽  
Test Cases ◽  
Mutation Operator ◽  
Optimal Solutions ◽  
Crossover Operator ◽  
Network Systems

Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.

scholarly journals A Hybrid Quantum Evolutionary Algorithm with Improved Decoding Scheme for a Robotic Flow Shop Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Weidong Lei ◽  
Hervé Manier ◽  
Marié-Ange Manier ◽  
Xinping Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Manufacturing Systems ◽  
Flow Shop ◽  
Population Diversity ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problem ◽  
Genetic Operators ◽  
Mutation Operators ◽  
Comparison Results

We aim at solving the cyclic scheduling problem with a single robot and flexible processing times in a robotic flow shop, which is a well-known optimization problem in advanced manufacturing systems. The objective of the problem is to find an optimal robot move sequence such that the throughput rate is maximized. We propose a hybrid algorithm based on the Quantum-Inspired Evolutionary Algorithm (QEA) and genetic operators for solving the problem. The algorithm integrates three different decoding strategies to convert quantum individuals into robot move sequences. The Q-gate is applied to update the states of Q-bits in each individual. Besides, crossover and mutation operators with adaptive probabilities are used to increase the population diversity. A repairing procedure is proposed to deal with infeasible individuals. Comparison results on both benchmark and randomly generated instances demonstrate that the proposed algorithm is more effective in solving the studied problem in terms of solution quality and computational time.

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.

scholarly journals Selected Genetic Algorithms for Vehicle Routing Problem Solving

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3147
Author(s):  
Joanna Ochelska-Mierzejewska ◽  
Aneta Poniszewska-Marańda ◽  
Witold Marańda
Keyword(s):  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Real Life ◽  
Economic Benefits ◽  
Added Value ◽  
Genetic Operators ◽  
Routing Problem ◽  
Starting Point

The traveling salesman problem (TSP) consists of finding the shortest way between cities, which passes through all cities and returns to the starting point, given the distance between cities. The Vehicle Routing Problem (VRP) is the issue of defining the assumptions and limitations in mapping routes for vehicles performing certain operational activities. It is a major problem in logistics transportation. In specific areas of business, where transportation can be perceived as added value to the product, it is estimated that its optimization can lower costs up to 25% in total. The economic benefits for more open markets are a key point for VRP. This paper discusses the metaheuristics usage for solving the vehicle routing problem with special attention toward Genetic Algorithms (GAs). Metaheuristic algorithms are selected to solve the vehicle routing problem, where GA is implemented as our primary metaheuristic algorithm. GA belongs to the evolutionary algorithm (EA) family, which works on a “survival of the fittest” mechanism. This paper presents the idea of implementing different genetic operators, modified for usage with the VRP, and performs experiments to determine the best combination of genetic operators for solving the VRP and to find optimal solutions for large-scale real-life examples of the VRP.

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