Crossover Operator Of Genetic Algorithm Research Articles

Solving 2-way graph partitioning problem using genetic algorithm based on uniform design sampling

Genetic Algorithm(GA) is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness.Based on the results,the crossover operation in GA was redesigned by using the principle of random uniform design sampling and combining the locale search strategy.Then a new GA called Genetic Algorithm based on Uniform Design Sampling(UDS) was presented.The new GA was applied to solve the 2-way graph partitioning problem.Compared to simple GA and good point GA for solving this problem,the simulation results show that the new GA has superiority in terms of speed and accuracy and overcomes premature convergence.

A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting

In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy.

A genetic fuzzy [formula omitted]-Modes algorithm for clustering categorical data

The fuzzy k-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), 446–452] is very effective for identifying cluster structures from categorical data sets. However, the algorithm may stop at locally optimal solutions. In order to search for appropriate fuzzy membership matrices which can minimize the fuzzy objective function, we present a hybrid genetic fuzzy k-Modes algorithm in this paper. To circumvent the expensive crossover operator in genetic algorithms (GAs), we hybridize GA with the fuzzy k-Modes algorithm and define the crossover operator as a one-step fuzzy k-Modes algorithm. Experiments on two real data sets are carried out to illustrate the performance of the proposed algorithm.

周期関数最適化のための遺伝的アルゴリズムの交叉演算

周期関数最適化のための遺伝的アルゴリズムの交叉演算

Phase transitions and symmetry breaking in genetic algorithms with crossover

In this paper, we consider the role of the crossover operator in genetic algorithms. Specifically, we study optimisation problems that exhibit many local optima and consider how crossover affects the rate at which the population breaks the symmetry of the problem. As an example of such a problem, we consider the subset sum problem. In doing so, we demonstrate a previously unobserved phenomenon, whereby the genetic algorithm with crossover exhibits a critical mutation rate, at which its performance sharply diverges from that of the genetic algorithm without crossover. At this critical mutation rate, the genetic algorithm with crossover exhibits a rapid increase in population diversity. We calculate the details of this phenomenon on a simple instance of the subset sum problem and show that it is a classic phase transition between ordered and disordered populations. Finally, we show that this critical mutation rate corresponds to the transition between the genetic algorithm accelerating or preventing symmetry breaking and that the critical mutation rate represents an optimum in terms of the balance of exploration and exploitation within the algorithm.

Improvement of crossover operator in genetic algorithm for reactive scheduling(Manufacturing systems and Scheduling)

Improvement of crossover operator in genetic algorithm for reactive scheduling(Manufacturing systems and Scheduling)

Training recurrent neural networks by using parallel tabu search algorithm based on crossover operation

There are several heuristic optimisation techniques used for numeric optimisation problems such as genetic algorithms, neural networks, simulated annealing, ant colony and tabu search algorithms. Tabu search is a quite promising search technique for non-linear numeric problems, especially for the problems where an optimal solution must be determined on-line. However, the converging speed of the basic tabu search to the global optimum is the initial solution dependent since it is a form of iterative search. In order to overcome this drawback of basic tabu search, this paper proposes a new parallel model for the tabu search based on the crossover operator of genetic algorithms. After the performance of the proposed model was evaluated for the well-known numeric test problems, it is applied to training recurrent neural networks to identify linear and non-linear dynamic plants and the results are discussed.

Automated Reservoir Model Selection in Well-Test Interpretation

Summary Technological achievements in the area of well testing, such as permanent downhole gauges, demand automated techniques to cope with the large amounts of data acquired. In such an application, the need to interpret large quantities of data with little human intervention suggests the desirability of automated model recognition. Also, in some cases, the characteristic behavior of the pressure or its derivative curves for specific models may be hidden behind noise, or human bias may lead to the selection of an invalid or inappropriate model. This paper demonstrates an approach based on a genetic algorithm (GA) that is able to select the most probable reservoir model from among a set of candidate models, consistent with a given set of pressure-transient data. The type of reservoir model to be used is defined as a variable and is estimated together with the other unknown model parameters (permeability, skin, etc.). Several reservoir models are used simultaneously in the regression process. GA populations consist of individuals that represent parameters for different models. As the GA iterates, individuals that belong to the most likely reservoir model dominate the population, while less likely models become extinct. Because different models may require different numbers of parameters, the solution vectors have varying lengths. The GA is able to cope with such solution vectors of differing size. Information exchange (GA crossover operator) is allowed only between parameters that are physically related. Alternatively, we illustrate the use of the GA as a preprocessor for conventional gradient-based algorithms such as Levenberg-Marquardt.1 When combined with the GA, the dependency of such conventional algorithms on the initial guess is reduced, and the overall regression performance is improved. For automated interpretations in which the model is already known, this method allows us to eliminate the initial guess-determination step. Tests on real and synthetic pressure-transient data indicated that the proposed method was able to select the correct reservoir model. The method revealed hidden implications of the pressure transient that may otherwise have been overlooked because of noise. As a preprocessor for more conventional nonlinear regression approaches, applying the GA to a number of noisy pressure-transient tests demonstrated that the method is robust and efficient.

An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification

We study the performance of genetic algorithm (GA) based artificial neural network (ANN) for different crossover operators. We use simulated and real life data to test the performance of GA-based ANN. Our results indicate that arithmetic crossover operator may be a suitable crossover operator for GA based ANN. Scope and purpose Genetic algorithm based artificial neural networks are used in several classification and forecasting applications. Among several genetic algorithm design operators, crossover plays an important role for convergence to the global heuristic solution. Several crossover operators exist, and selection of a crossover operator is an important design issue confronted by most researchers. The current study investigates the impact of different crossover operators on the performance of genetic algorithm based artificial neural networks.

ＦＴＬへの１個流しオフライン生産シミュレータ―環状型個体表現を用いた生産比率順守―

This paper describes the research about the off-line production system's simulator connected Genetic Algorithm (GA) to realize a single part input into a variety production line. In this paper, a recurring individual is proposed. This simulator expresses parts input sequence as a recurring individual in GA and gives a crossover and a mutation not to change a goal production ratio for each part. The recurring individual has the characteristics efficiently to find the crossover point in the crossover operation of GA. The developed system was applied to the FTL model in a computer. As a result, the outputs by using the inputs parts sequence acquired by the developed system are 30 - 60 more than those of the conventional way.

Akış Tipi Çizelgeleme Problemlerinin Genetik Algoritma Yardımı ile Çözümünde Uygun Çaprazlama Operatörünün Belirlenmesi

In this study crossover operators of Genetic Algorithms are tested for flowshop scheduling problems which are in NP-hard class and the most effective operator is determined. Six crossover operators are tested on different scaled flowshop scheduling problems with long processing times. Problems are examined in two categories: 2 machine and multi machine problems. In 2-machine problems six different scaled problems were used which are produced randomly. For multi-machine problems seven different scaled reference problems were used which are produced by J. Carlier. The most effective crossover operators are determined for both categories according to the results of 2050 experiments.

DOMAIN APPROXIMATION AND DETERMINISTIC PROGRESSION IN GENETIC CROSSOVER

The fitness difference between two strings is usually neglected by most Genetic Algorithm crossover operators. The neglect of this useful information often results in the less efficient propagation of search trials toward fitness improved regions, and leads to a general increase in the number of function evaluations required to converge to a solution. An alternative crossover strategy is presented in this paper which considers this information, often significantly accelerating and enhancing the search process. The presented strategy involves components of solution domain approximation and the deterministic estimation of the “optimal” genetic composition for progeny. The implementation of this crossover scheme has empirically demonstrated its effectiveness in significantly reducing the required number of function evaluations, and improving solution quality compared with some conventional crossover strategies

A Novel Genetic Algorithm and its Application in Fuzzy Variable Structure Control of Fuel Cell

A novel genetic algorithm (GA) is proposed; a ranking genetic algorithm with improved crossover operator. The whole colony is divided into several sub-colonies, and each sub-colony is called a family, which is represented by its best individual. This algorithm includes two levels of structure: the family level and the harmonizing level. The families are parallel during the process of evolution. The harmonizing level ranks all families based on their fitness values, and transports the best individual of the first-rank family to low-grade families so as to accelerate their evolution. Two levels of competition are constructed; one among individuals of a family, and the other among families. The competition within a family is accomplished by a genetic algorithm with improved crossover operator. A family's mutation probability is determined by its relative competitive power. In this way, a rapid and global convergence to the optimum goal is obtained. The GA crossover operator is improved for the case of floating point operations. The improved crossover operator can generate child individuals at random within the space of the supercube, which enhances the space searching rate and precision. Finally, the proposed novel GA is applied to the fuzzy-variable structure control (FVSC) system of a molten carbonate fuel cell (MCFC). The simulation results are satisfying.

Performance analysis for crossover operators of genetic algorithm

In this paper, we deal with promising crossover operators developed in the genetic algorithm (GA) and analyze the performance of these crossovers on the traveling salesman problem (TSP) which is one of the most popular NP-hard problems. Many crossovers that efficiently generate good solutions have been proposed, and the performance of each crossover was evaluated using the framework of the simple GA. Therefore, for practical use, we propose a framework based on the GA combined with a local search algorithm, and analyze three crossovers for the TSP, the maximal preservative crossover of Mühlenbein et al., the improved edge recombination crossover of Starkweather et al., and the complete subtour exchange crossover recently proposed by us. From our results, we show that our crossover obtains better-quality solutions than the others and that the solutions obtained using the framework were little influenced by the probability of the mutational operator. © 1999 Scripta Technica, Syst Comp Jpn, 30(2): 20–30, 1999

Performance analysis for crossover operators of genetic algorithm

In this paper, we deal with promising crossover operators developed in the genetic algorithm (GA) and analyze the performance of these crossovers on the traveling salesman problem (TSP) which is one of the most popular NP-hard problems. Many crossovers that efficiently generate good solutions have been proposed, and the performance of each crossover was evaluated using the framework of the simple GA. Therefore, for practical use, we propose a framework based on the GA combined with a local search algorithm, and analyze three crossovers for the TSP, the maximal preservative crossover of Muhlenbein et al., the improved edge recombination crossover of Starkweather et al., and the complete subtour exchange crossover recently proposed by us. From our results, we show that our crossover obtains better-quality solutions than the others and that the solutions obtained using the framework were little influenced by the probability of the mutational operator. © 1999 Scripta Technica, Syst Comp Jpn, 30(2): 20–30, 1999

Theoretical and Experimental Study of Crossover Operators of Genetic Algorithms

This paper is concerned with crossover operators for genetic algorithms (GAs) which are used to solve problems based on real numbers. First, a classification of the operators is introduced, dividing crossover into a vector-level and a variable-level operator. The theoretical study of variable-level operators for binary coded GAs leads to the discovery of two properties, which are used to define certain characteristics of crossover operators used by real-number encoded GAs. For variable-level operators, the experimental distributions of the offspring variables of given pairs of parent variables are then found. Finally, an experimental comparison of crossover operator performance is carried out.

Function optimization using an adaptive crossover operator based on locality

This paper proposes a new crossover operator in genetic algorithms for function optimization. The proposed Adaptive Crossover Operator (ACO) restricts the crossover range by using a bias_value. The bias_value is computed by the fitness function value, the performance ratio, and the number of generations. This ACO can reduce the computational complexity of obtaining the global optimum. By maintaining diversity in the population and sustaining the balance between exploration and exploitation, it can also prevent the genetic algorithm from getting stuck at a local optimum. As the generations progress, the ACO achieves a local improvement on a part of a chromosome which is restricted by the bias_value. The experiment demonstrates that a genetic algorithm using the ACO performs better than a genetic algorithm using a standard crossover in optimizing several functions.

ある製品投入順序問題における遺伝アルゴリズムの交差規則のロバスト性

ある製品投入順序問題における遺伝アルゴリズムの交差規則のロバスト性

Genetic algorithm crossover operators for ordering applications

In this paper, we compare the performance of several crossover operators, including two new operators and a new faster formulation of a previously published operator. This new formulation performs better than the other operators we have tested while taking no more computation time. In addition, with practical applications in mind, we show how the use of problem specific information can improve the performance of the Genetic Algorithm and we describe a method for designing problem specific crossover incorporating a novel tie-breaking algorithm.

Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space. Part II: Analysis of the diversification role of crossover

For pt. I see ibid., p. 102-19. In this part of the paper the authors concentrate on the unique diversification role of the crossover operator in genetic algorithms. The explorative behavior of a generic crossover operator is revealed through a detailed large-sample analysis. Recursive equations for the population distributions are derived for a uniform crossover operator in multi-dimensional continuous space, showing how the crossover operator probes new regions of the solution space while keeping the population within the feasible region. The results of this analysis can be extended to the setting of a discrete space in a straightforward manner, shedding much light on the understanding of the essential role of crossover in genetic algorithms.