A Genetic Algorithm Based on Modified Selection Operator and Crossover Operator
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In order to solve the conflict between algorithm convergence and the best local answer effectively,puts forward an improved genetic algorithm with a modified selection operator and a modified crossover operator.It can increase the probability of the best answer and well avoid approaching the best local solution by using the modified selection operator,it also increased the probability of finding the best answer,and using the modified crossover operator can speed up the convergence rate,thus shortening the time to find the best answer.The experimental result indicates that the two modified operators' combination can converge to the best answer at higher speed,so it can well solve the contradiction between the convergence rate of genetic algorithm and the best local solution.Keywords:
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Prematurity and slow convergence are two difficult problems in genetic algorithm, a new crossover is proposed, named hybrid crossover operator based on pattern, to gain a better convergence to the optimal solution. To retain the diversity of population, the approach of pattern is used, together with the behavior of antitone. The new method can be used for those application problems which are wanted to reach their best value quickly. More experiments show that the new crossover can find the global optimal solution and improve convergence ratio obviously.
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The fitness value approximation steers the location of progeny,that obtain optimal solutions quickly.However,it was fallen into the local optimal solutions easily.An improved strategy was present,the individual for crossover operator was accertained,the regions with high fitness were explat.Introducing the process to generate new ones at different rate for every individual,it keep population diversity.Experiment results show that the improved method is useful and effective,and promote the convergence rate in genetic algorithm.
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<p>Route planning is an important part of road network. To select an optimized route several factors such as flow of traffic, speed limits of road. are concerned. Total cost of such a network depends on the number of junctions between the source and the destination. Due to the growth of the nodes in the network it becomes a tough job to determine the exact path using deterministic algorithms so in such cases genetic algorithms (GA) plays a vital role to find the optimized route. Crossover is an important operator ingenetic algorithm. The efficiency of thegenetic algorithmis directlyinfluenced by the time of a crossover operation. In this paper a new crossoveroperator closest-node pairing crossover (CNPC) is recommended which is explicitly designed to improve the performance of the genetic algorithm compared to other well-known crossover operators such as point based crossover and order crossover. The distance aspect of the network problem has been exploited in this crossover operator. This proposed technique gives a better result compared to the other crossover operator with the fitness value of 0.0048. The CNPC operator gives better rate of convergence compared to the other crossover operators.</p>
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Considering the deficiency of the traditional crossover operator of Genetic Algorithms (GAs) in global searching, the partheno-crossover operator is proposed in this paper. The partheno-crossover, a new crossover operator, still uses the traditional method of crossover. The individual of the population doesn't cross over the individual of the same population but the individual of the completely different with it. When these two individuals have many offspring, only the fittest offspring can replace his father's position in the population. So this offspring either inherits the father's good gene fragments or have a better gene fragments. It both ensures that the population develops to excellent direction, and avoids that the population converges to a local optimal solution. Compared to the Standard Genetic Algorithm (SGA), the traditional improved Genetic Algorithm (TIGA) and the Partheno-Genetic Algorithm (PGA), the improved Genetic Algorithm used the partheno-crossover operator (PCGA) allows better stability of global convergence and some comparative results relative to the optimization of test functions has increase of 10 times, even Several decuples.
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In view of the weak ability of Climbing of the basic genetic algorithm,the premature feature,and low searching efficiency,this paper put forward an optimal design of S-box based on genetic algorithm.In the initial population of the production process,by adding a priori knowledge generated S-box with partial advantageous performance,the speed and effect of convergence were improved to some degree.In genetic operator,using the best individual preservation method selection strategy,additional computing can be greatly reduced.Through the simulation experiments and results analysis,this algorithm is verified by the constructed S-box in cryptography properties.Convergence speed and fitness values have a very good improvement.
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In order to solve the conflict between algorithm convergence and diversity,after analyzing the structure of operator,this paper puts forward an improved genetic algorithm. This algorithm’s core lies on retaining the outstanding individual through parents,offsprings competition and improved crossover operator to guarantee convergence. On the other hand,carrying on the similarity examination to the crossover gene section based on the Haming distance to enhance validity. Finally,using the self - identify high mutation operator based on gene position diverse to improve the population diversity. The experimental results show that the improved operator improves the ability of searching an optimum solution and increases the convergent speed.
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This paper proposes an improved real-coded genetic algorithm(RCGA) with a new crossover operator and a new mutation operator. The crossover operator is designed, based on the evolutionary direction provided by two parents, the fitness ratio of two parents, and the distance between two parents. This crossover operator can improve the convergence speed of RCGAs by using the heuristic information mentioned above. Moreover, the proposed mutation operator, which utilizes the entropy information of every gene locus in chromosomes, can prevent the premature convergence of RCGAs. Experiments on benchmark test functions with different hardness describe the effectiveness of the improved RCGA.
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Genetic Matching Pursuit Algorithm can raise the speed of finding the best atom,but the speed of the algorithm is low because of the crossover reducing the convergence.The algorithm is improved by the Laplace Crossover in which the parent of the Laplace distribution density function coefficients replace the arithmetic crossover operator coefficients,through the parent control of offspring production.Simulation results show that the improved genetic matching algorithms is effect from the residual energy and searching time.
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Genetic Algorithm (GA) represents robust, adaptive method successfully applied to various optimization problems. To evaluate the performance of the genetic algorithm, it is common to use some kind of test functions. However, the no free lunch theorem states it is not possible to find the perfect, universal solver algorithm. To evaluate the algorithm, it is necessary to characterize the type of problems for which that algorithm is suitable. That would allow conclusions about the performance of the algorithm based on the class of a problem. In performance of a genetic algorithm, crossover operator has an invaluable role. To better understand performance of a genetic algorithm in a whole, it is necessary to understand the role of the crossover operator. The purpose of this paper is to compare larger set of crossover operators on the same test problems and evaluate their's efficiency. Results presented here confirm that uniform and two-point crossover operators give the best results but also show some interesting comparisons between less used crossover operators like segmented or half-uniform crossover.
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Abstract In this paper we propose a new crossover operator for real coded evolutionary algorithms that is based on a parabolic probability density function. This density function depends on two real parameters α and β which have the capacity to achieve exploration and exploitation dynamically during the evolutionary process in relation to the best individuals. In other words, the proposed crossover operator is able to handle the generational diversity of the population in such a way that it can either generate additional population diversity, therefore allowing exploration to take effect, or use the diversity previously generated to exploit the better solutions. In order to test the performance of this crossover, we have used a set of test functions and have made a comparative study of the proposed crossover against other classic crossover operators. The analysis of the results allows us to affirm that the proposed operator displays a very suitable behavior, although, it should be noted that it offers a b...
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