Computational Complexity Analysis of Selective Breeding Algorithm

Computational Complexity Analysis of Selective Breeding Algorithm M. Chandrasekaran Vels University P. Sriramya Saveetha School of Engineering B. Parvathavarthini St. Joseph’s College of Engineering M. Saravanamanikandan St. Joseph’s College of Engineering

In modern years, there has been growing importance in the design, analysis and to resolve extremely complex problems. Because of the complexity of problem variants and the difficult nature of the problems they deal with, it is arguably impracticable in the majority time to build appropriate guarantees about the number of fitness evaluations needed for an algorithm to and an optimal solution. In such situations, heuristic algorithms can solve approximate solutions; however suitable time and space complication take part an important role. In present, all recognized algorithms for NP-complete problems are requiring time that's exponential within the problem size. The acknowledged NP-hardness results imply that for several combinatorial optimization problems there are no efficient algorithms that realize a best resolution, or maybe a close to best resolution, on each instance. The study Computational Complexity Analysis of Selective Breeding algorithm involves both an algorithmic issue and a theoretical challenge and the excellence of a heuristic.
7 2014 172 175 https://www.scientific.net/PolicyAndEthics/PublishingPolicies https://www.scientific.net/license/TDM_Licenser.pdf 10.4028/www.scientific.net/AMM.591.172 https://www.scientific.net/AMM.591.172 https://www.scientific.net/AMM.591.172.pdf Zitzler,E., Deb, K., Lothar,T., Comparison of multiobjective evolutionary algorithms: Empirical results, Evolutionary Computation Journal, 8(2)(2000) pp.125-148. 10.1162/106365600568202 10.1287/inte.32.3.30.39 Craig A., Tovey, Tutorial on Computational Complexity , Interfaces, Vol. 32, No. 3, May–June 2002, pp.30-61. P. Sriramya, B. Parvathavarthini and T. Balamurugan, A Novel Evolutionary Selective Breeding Algorithm and its Application, Asian Journal of Scientific Research, DOI: 10. 3923/ajsr. (2012). Yang S., Dingwei Wang, A new adaptive neural network and heuristics hybrid approach for job shop scheduling, Computers and operations Research, 28(2001)pp.955-971. 10.1016/s0305-0548(00)00018-6 Taillard E., Benchmarks for basic scheduling problems, European Journal of Operational Research, 64(1993) pp.278-285. 10.1016/0377-2217(93)90182-m Pezzella F., Emanuela Merelli, A tabu search method guided by shifting bottleneck for the job shop scheduling problem, European Journal of Operational Research 120 (2000), pp.297-310. 10.1016/s0377-2217(99)00158-7