Genocop, by Zbigniew Michalewicz, is a genetic algorithm-based program for constrained and unconstrained optimization, written in C. The Genocop system aims at finding a global optimum (minimum or maximum: this is one of the input parameters) of a function; additional linear constraints (equations and inequalities) can be specified as well. The current version of Genocop should run without changes on any BSD-UN*X system (preferably on a Sun SPARC machine). This program can also be run on a DOS system. This software is copyright by Zbigniew Michalewicz. Permission is granted to copy and use the software for scientific, noncommercial purposes only. The software is provided ”as is”, i.e., without any warranties.

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  1. Jana, Dipak Kumar; Das, Barun: A two-storage multi-item inventory model with hybrid number and nested price discount via hybrid heuristic algorithm (2017)
  2. Lostado-Lorza, Ruben; Escribano-Garcia, Ruben; Fernandez-Martinez, Roberto; Illera-cueva, Marcos; Mac Donald, Bryan J.: Using the finite element method and data mining techniques as an alternative method to determine the maximum load capacity in tapered roller bearings (2017)
  3. Martinez, Nadia; Anahideh, Hadis; Rosenberger, Jay M.; Martinez, Diana; Chen, Victoria C.P.; Wang, Bo Ping: Global optimization of non-convex piecewise linear regression splines (2017)
  4. Vömel, Christof; de Lorenzi, Flavio; Beer, Samuel; Fuchs, Erwin: The secret life of keys: on the calculation of mechanical lock systems (2017)
  5. Ahmed, Zakir Hussain: Experimental analysis of crossover and mutation operators on the quadratic assignment problem (2016)
  6. Berres, S.; Coronel, A.; Lagos, R.; Sepúlveda, M.: Performance of a real coded genetic algorithm for the calibration of scalar conservation laws (2016)
  7. Chakraborti, Debjani: Evolutionary technique based goal programming approach to chance constrained interval valued bilevel programming problems (2016)
  8. Dutta, S.; Acharya, S.; Mishra, Rajashree: Genetic algorithm based fuzzy stochastic transportation programming problem with continuous random variables (2016)
  9. Huajun, Zhang; Jin, Zhao; Hui, Luo: A method combining genetic algorithm with simultaneous perturbation stochastic approximation for linearly constrained stochastic optimization problems (2016)
  10. Karasakal, Esra; Silav, Ahmet: A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage (2016)
  11. Li, Xiang; Sun, Guohua; Li, Yongjian: A multi-period ordering and clearance pricing model considering the competition between new and out-of-season products (2016)
  12. Manikumar, T.; Sanjeev Kumar, A.John; Maruthamuthu, R.: Automated test data generation for branch testing using incremental genetic algorithm (2016)
  13. Pan, Quan-Ke: An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling (2016)
  14. Papoutsis-Kiachagias, E.M.; Giannakoglou, K.C.: Continuous adjoint methods for turbulent flows, applied to shape and topology optimization: industrial applications (2016)
  15. Tang, Zhili; Zhang, Lianhe: Nash equilibrium and multi criterion aerodynamic optimization (2016)
  16. Tawhid, Mohamed A.; Ali, Ahmed F.: Simplex particle swarm optimization with arithmetical crossover for solving global optimization problems (2016)
  17. Waligóra, Grzegorz: Comparative analysis of some metaheuristics for discrete-continuous project scheduling with activities of identical processing rates (2016)
  18. Zhou, Xiao-gen; Zhang, Gui-jun; Hao, Xiao-hu; Yu, Li: A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization (2016)
  19. Angelo, Jaqueline S.; Barbosa, Helio J.C.: A study on the use of heuristics to solve a bilevel programming problem (2015)
  20. Azad, Md.Abul Kalam; Rocha, Ana Maria A.C.; Fernandes, Edite M.G.P.: Solving large 0-1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm (2015)

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