• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.317-322
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• 2002

The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.55-61
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• 2020

Genetic Algorithm(GA) is applied to a problem that could not figure out its solution in a straightway. It is called as NP-hard problem. GA requires a high-performance system to be run on since the high-cost operations are needed such as crossover, selection, and mutation. Moreover, the scale of the problem domain is normally huge. That is why the straightway cannot be applied. To reduce the drawback of high-cost requirements, we try to answer if all the operations including mutation are necessary for all cases. In the experiment, we set up two cases of with/without mutation operations and gather the number of generations and the fitness of a solution. The subject in the experiment is Travelling Salesman Problem(TSP), which is one of the popular problems solved by GA. As a result, the cases with mutation operation are not faster and the solution is fitter than the case with mutation operation. From the result, the conclusion is that mutation operation does not always need for a better solution in a faster way.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.217-222
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• 2011

Genetic algorithms are one of the most important methods used to solve the Traveling Salesman Problem. Therefore, many researchers have tried to improve the Genetic Algorithm by using different methods and operations in order to find the optimal solution within reasonable time. This paper intends to find a new approach that adopts an improved genetic algorithm to solve the Traveling Salesman Problem, and compare with the well known heuristic method, namely, Kohonen Self-Organizing Map by using different data sets of symmetric TSP from TSPLIB. In order to improve the search process for the optimal solution, the proposed approach consists of three strategies: two separate tour segments sets, the improved crossover operator, and the improved mutation operator. The two separate tour segments sets are construction heuristic which produces tour of the first generation with low cost. The improved crossover operator finds the candidate fine tour segments in parents and preserves them for descendants. The mutation operator is an operator which can optimize a chromosome with mutation successfully by altering the mutation probability dynamically. The two improved operators can be used to avoid the premature convergence. Simulation experiments are executed to investigate the quality of the solution and convergence speed by using a representative set of test problems taken from TSPLIB. The results of a comparison between the new approach using the improved genetic algorithm and the Kohonen Self-Organizing Map show that the new approach yields better results for problems up to 200 cities.

• Journal of Interdisciplinary Genomics
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• v.4 no.1
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• pp.15-18
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• 2022

Hemophilia A is a rare X-linked congenital deficiency of clotting factor VIII (FVIII) that is traditionally diagnosed by measuring FVIII activity. Various mutations of the FVIII gene have been reported and they influence on the FVIII protein structure. A deficiency of or reduction in FVIII protein manifests as spontaneous or induced bleeding depending on the disease severity. Mutations of the FVIII gene provide important information on the severity of disease and inhibitor development. FVIII mutations also affect the discrepant activities found using different FVIII assays. FVIII activity is affected differently depending on the mutation site. Long-range PCR is commonly used to detect intron 22 inversion, the most common mutation in severe hemophilia. However, point mutations are also common in patients with hemophilia, and direct Sanger sequencing and copy number variant analysis are being used to screen for full mutations in the FVIII gene. Advances in molecular genetic methods, such as next-generation sequencing, may enable accurate analysis of mutations in the factor VIII gene, which may be useful in the diagnosis of mild to moderate hemophilia. Genetic analysis is also useful in diagnosing carriers and managing bleeding control. This review discusses the current knowledge about mutations in hemophilia and focuses on the clinical aspects associated with these mutations and the importance of genetic analysis.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.310-315
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• 2005

This paper proposes a fast optimization method by combining queen-bee evolution and derivative evaluation in genetic algorithms. These two operations make it possible for genetic algorithms to focus on highly fitted individuals and rapidly evolved individuals, respectively. Even though the two operations can also increase the probability that genetic algorithms fall into premature convergence phenomenon, that can be controlled by strong mutation rates. That is, the two operations and the strong mutation strengthen exploitation and exploration of the genetic algorithms, respectively. As a result, the genetic algorithm employing queen-bee evolution and derivative evaluation finds optimum solutions more quickly than those employing one of them. This was proved by experiments with one pattern matching problem and two function optimization problems.

• Journal of Genetic Medicine
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• v.7 no.1
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• pp.1-8
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• 2010

Hemophilia A is a sex-linked recessive coagulation disorder associated with diverse mutations of the factor VIII gene and a variety of phenotypes. The type of mutation involved dictates the activity of factor VIII, and in turn the severity of bleeding episodes and development of alloantibodies against factor VIII (inhibitors). Missense mutations are the most common genetic risk factors for hemophilia A, especially mild to moderate cases, but carry the lowest risk for inhibitor development. On the other hand, intron 22 inversion is the most common mutation associated with severe hemophilia A and is associated with high risk of inhibitor formation. Large deletions and nonsense mutations are also associated with high risk of inhibitor development. Additional mutations associated with hemophilia A include frameshift and splice site mutations. It is therefore valuable to assess the mutational backgrounds of hemophilia A patients in order to to interpret their symptoms and manage their health problems.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.4
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• pp.490-499
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• 1999

Vehicle routing problem(VRP) is known to be NP-hard problem, and good heuristic algorithm needs to be developed. To develop a heuristic algorithm for the VRP, this study suggests a parallel genetic algorithm(PGA), which determines each vehicle route in order to minimize the transportation costs. The PGA developed in this study uses two dimensional array chromosomes, which rows represent each vehicle route. The PGA uses new genetic operators. New mutation operator is composed of internal and external operators. internal mutation swaps customer locations within a vehicle routing, and external mutation swaps customer locations between vehicles. Ten problems were solved using this algorithm and showed good results in a relatively short time.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.37-44
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• 2003

In this paper, we investigated the performance of both DNA coding method and Genetic Algorithm(GA) in numeric pattern (from 0 to 9) recognition. The performance of the DNA coding method is compared to the that of the GA. GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by Adenine(A), Cytosine(C), Guanine(G) and Thymine(T). To compare the performance of both method, the same genetic operators(crossover and mutation) are applied and the probabilities of crossover and mutation are set the same values. The results show that the DNA coding method has better performance over GA. The reasons for this outstanding performance are multiple candidate solution presentation in one string and variable solution string length.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.3
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• pp.73-82
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• 1999

In this paper, we address a single machine non-preemptive n-job scheduling problem to minimize the sum of earliness and tardiness with different release times and due dates. To solve the problem, we propose a genetic algorithm with new crossover and mutation operators to find the job sequencing. For the proposed genetic algorithm, the optimal pair of crossover and mutation rates is investigated. To illustrate the suitability of genetic algorithm, solutions of genetic algorithm are compared with solutions of exhaustive enumeration method in small size problems and tabu search method in large size problems. Computational results demonstrate that the proposed genetic algorithm provides the near-optimal job sequencing in the real world problem.

• Journal of Microbiology
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• v.34 no.2
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• pp.166-171
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• 1996

Genetic analysis was conducted on newly isolated coliphages form soil by using a RAPD assay. From the initial result, the coliphages were turned out to be different form one another but were closely related to .psi..lambda. due to the fact that they shared the samed RAPD maker in which other T phage testings failed to show. By using the primers EC01 or EC02, a fast genetic mutation of .psi.C1 was found by producing specific RAPD markers on the phages from the first filial progeny to the second filial progeny. When we made a RAPD assay with combined primers (EC01, EC05 and EC08), the genetic mutation was again confirmed in .psi.C1. The assay detection showed mutations in other coliphages such as .psi.C2 and .psi.C3 by revealing specific RAPD bands among different progeny phages, where genetic instability of the coliphages in implied.