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Thangamani, C.
- Evaluation of F1 Hybrids in Bitter Gourd (Momordica charantia L.) for Yield and Quality
Authors
1 Department of Vegetable Crops, Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore – 641 003, IN
Source
Journal of Horticultural Sciences, Vol 6, No 2 (2011), Pagination: 105-108Abstract
To study the combining ability and heterosis for yield and quality characters, full diallel analysis was carried out in bitter gourd during January - April 2008 (Thai pattam), with 10 diversified parents, at Research Farm, Horticultural College and Research Institute, TNAU, Coimbatore. Parental mean and gca effects revealed that the parents Preethi, CO-1, MC-30, Uchha Bolder, Green Long and MC-105 were the best genotypes for improvement of yield, combined with quality characters. Hybrids, viz., Preethi x MC-30, KR x USL, MC-105 x MC-10 and Priyanka x CO-1 registered favourable values for mean, significant sca and standard heterosis for yield and quality parameters. Hence, these hybrids are recommended for commercial exploitation of heterosis. Comparison of parental gca and sca of hybrids revealed that hybridization between good x good, good x poor, medium x poor and poor x good combiners gave rise to hybrids with significant sca effects. Considering the mean performance, sca and standard heterosis, hybrid 'Preethi x MC-30' registered favourable values for the most important characters like earliness, number of fruits, fruit yield and quality. Top performing F1 hybrids can be tested over seasons and locations for assessing stability for high yield and quality.Keywords
Bitter Gourd, Momordica charantia, Mean Performance, Combining Ability, Standard Heterosis.Full Text
- Analysis of Variability for Qualitative and Quantitative Traits in Coleus forskohlii Briq.
Authors
1 Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore-641 003, IN
2 Directorate of Extension Education, TNAU, Coimbatore – 641 003, IN
Source
Journal of Horticultural Sciences, Vol 2, No 1 (2007), Pagination: 44-46Abstract
Thirty seven Coleus forskohlii genotypes collected from different regions of Tamil Nadu and Karnataka were subjected to diversity analysis based on NBPGR descriptors. Eleven qualitative and fourteen quantitative traits of C. forskohlii were evaluated to assess the morphological variations available among the collected genotypes. For qualitative traits, a large number of genotypes out of 37 clustered together at 74 % similarity in four different groups. The dendrogram contract based on fourteen quantitative traits for the same set of genotypes did not reveal a clear pattern in grouping and the genotypes were grouped into ten different clusters. Cluster analysis of various sets of data revealed different groups of genotypes for each of the data set. A poor congruence observed among data sets of qualitative and quantitative traits in the comparison indicated that the morphological traits are not suitable for precise discrimination of closely related genotypes in C. forskohlii.Keywords
Coleus forskohlii, Morphological Traits, Cluster Analysis.Full Text
- A Novel Hybrid Genetic Algorithm with Weighted Crossover and Modified Particle Swarm Optimization
Authors
1 Department of Computer Science, Bharathiar University, Coimbatore-641046, IN
2 Department of Computer Science, Rejah Serfoji Government College, Thanjavur-613005, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 9, No 2 (2017), Pagination: 25-30Abstract
The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although these methods are approximate methods (i.e. their solutions are good, but probably not optimal), they do not require the derivatives of the objective function and constraints. Also, the heuristics use probabilistic transition rules instead of deterministic rules. Here, an evolutionary algorithm based on the hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), denoted by HGAPSO, is developed. Particle Swarm Optimization (PSO) is a very popular optimization technique, but it suffers from a major drawback of a possible premature convergence i.e. convergence to a local optimum and not to the global optimum. This paper attempts to improve on the reliability of PSO by addressing the drawback. This modified method would free PSO from local optimum solutions; enable it to progress towards the global optimum searching over wider area. So the probability, of not getting trapped into local optima gets enhanced which gives better assurance to the achieved solution. Experiments shows that the proposed method will provide better solution.
Keywords
Particle Swarm Optimization, Genetic Algorithm, Hybrid Algorithm, Modified Particle Swarm.
- Genetic Analysis in Bitter Gourd (Momordica charantia L.) for Yield and Component Characters
Authors
1 Horticultural Research Station (T.N.A.U.), Kodaikanal (T.N.), IN
Source
The Asian Journal of Horticulture, Vol 11, No 2 (2016), Pagination: 313-318Abstract
In a full diallel analysis ten parents of bitter gourd were used to study the inheritance of yield, yield contributing characters and quality characters. Data from the ten parents and their resultant 90 hybrids were analyzed. The estimates of D (additive effects) were significant for 13 characters in season I and 15 characters in season II out of 17 characters studied. The estimates of ‘H1’and‘H2’were positive and significant for all the traits. It indicated that there was unequal frequency of alleles at all loci. Further the proof for the unequal distribution of the alleles over loci was obtained by the ratio of H2/4H1. The estimates of ‘F’ were positive for all the traits studied. It indicated the pre-dominance of dominant alleles in the parents and this was supported by the ratio (KD/KR) of dominant to recessive alleles in the parents, the ratio was more than one for all the traits studied. The mean degree of dominance over all loci indicated over dominance for 11 characters in season I and 10 characters in season II. Narrow sense heritability estimates were high for seven out of 17 characters studied and for the remaining ten characters it was low in both the seasons. The preponderance of dominant gene action coupled with low narrow sense heritability observed for the traits viz., days to first male and female flower appearance, fruit flesh thickness (season II), number of fruits per vine (season I), yield of fruits per vine, ascorbic acid and iron content revealed the importance of heterosis breeding for simultaneous improvement of yield and quality characters.
Keywords
Momordica charantia L., Diallel Analysis, Gene Action, Earliness, Yield , Quality Traits.- An Efficient Hybrid of Continuous Ant Colony Optimization and Weighted Crossover Genetic Algorithm for Optimal Solution
Authors
1 Department of Computer Science, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, Rejah Serfoji Government College, Thanjavur, IN
Source
Fuzzy Systems, Vol 10, No 1 (2018), Pagination: 1-7Abstract
In real time applications the optimization problems that are hard to solve. To solve these kind of problems the algorithms should be specialized and applicable for large range of problems, or they are more general but rather inefficient. In which Evolutionary Algorithms (EA) are more popular which consist of several search heuristics by imitating some features of natural evolution and the social behavior of species. This heuristics algorithm are developed to solve optimization problem but it effectively fail because of convergence and computation time. To overcome this flaws a novel hybrid evolutionary algorithm as Genetic Algorithm (GA) - Continuous Ant Colony Optimization (CACO) is developed. The weighed crossover operation is introduced in genetic algorithm to select the crossover operator. CACO is utilized as a GA mutation then the GA output is given as an input to the CACO. Then the genetic algorithm undergoes the selection, crossover and it gives the result. Based on the comparative analysis, the performance results show the better efficiency and capabilities in finding the optimum solutions.Keywords
Evolutionary Algorithms, Optimization, Weighted Crossover, Genetic Algorithm (GA) and Ant Colony Optimization (ACO).References
- Elbeltagi, E., Hegazy, T. and Grierson, D., 2005. Comparison among five evolutionary-based optimization algorithms. Advanced engineering informatics, 19(1), pp.43-53.
- Gao, S., Zhang, Z. and Cao, C., 2010. A Novel Ant Colony Genetic Hybrid Algorithm. JSW, 5(11), pp.1179-1186.
- Aravindh, S., 2012. Hybrid of Ant Colony Optimization and Genetic Algorithm for Shortest Path in Wireless Mesh Networks. Journal of Global Research in Computer Science, 3(1), pp.31-34.
- Kovarık, O., 2006. Ant colony optimization for continuous problems (Doctoral dissertation, Msc. Thesis, Dept. of Electrical Engineering, University of Czech Technical).
- Aidov, A. and Dulikravich, G.S., 2009. Modified Continuous Ant Colony Algorithm. In 2nd International Congress of Serbian Society of Mechanics, Serbia.
- Devi, S.S. and Dhinakaran, S., 2013. Cross over and Mutation operations in GA-Genetic Algorithm. International Journal of computer and Organization Trends, 3(4).
- Kaya, Y. and Uyar, M., 2011. A novel crossover operator for genetic algorithms: Ring crossover. arXiv preprint arXiv:1105.0355.
- Mitras, B. and Aboo, A.K., Hybrid of Genetic Algorithm and Continuous Ant Colony Optimization for Optimum Solution.
- Tuncer, A. and Yildirim, M., 2012. Dynamic path planning of mobile robots with improved genetic algorithm. Computers & Electrical Engineering, 38(6), pp.1564-1572.
- Ciornei, I. and Kyriakides, E., 2012. Hybrid ant colony-genetic algorithm (GAAPI) for global continuous optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(1), pp.234-245.
- Ladkany, G.S. and Trabia, M.B., 2012. A genetic algorithm with weighted average normally-distributed arithmetic crossover and twinkling. Applied Mathematics, 3(10), p.1220.
- Socha, K. and Blum, C., 2007. An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Computing and Applications, 16(3), pp.235-247.
- Moradi, M.H. and Abedini, M., 2012. A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. International Journal of Electrical Power & Energy Systems, 34(1), pp.66-74.
- Angelova, M. and Pencheva, T., 2011. Tuning genetic algorithm parameters to improve convergence time. International Journal of Chemical Engineering, 2011.
- Roberge, V., Tarbouchi, M. and Labonté, G., 2013. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Transactions on Industrial Informatics, 9(1), pp.132-141.
- Whitley, D., 2014. An executable model of a simple genetic algorithm. Foundations of genetic algorithms, 2(1519), pp.45-62.
- Angelova, M., Atanassov, K. and Pencheva, T., 2012. Purposeful model parameters genesis in simple genetic algorithms. Computers & Mathematics with Applications, 64(3), pp.221-228.
- Sivanandam, S.N. and Deepa, S.N., 2007. Introduction to genetic algorithms. Springer Science & Business Media.
- Deep, K. and Thakur, M., 2007. A new crossover operator for real coded genetic algorithms. Applied mathematics and computation, 188(1), pp.895-911.
- Dorigo, M., 2006. Ant colony optimization-artificial ants as a computational intelligence technique. IEEE computational intelligence magazine, 1(4), pp.28-39.