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. Huajun, Zhang; Jin, Zhao; Hui, Luo: A method combining genetic algorithm with simultaneous perturbation stochastic approximation for linearly constrained stochastic optimization problems (2016)
  2. Karasakal, Esra; Silav, Ahmet: A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage (2016)
  3. 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)
  4. Baidin, Alexey Eduardovich: Determination of visual double star orbits by means of genetic algorithms (2015)
  5. Bravo, Yesnier; Luque, Gabriel; Alba, Enrique: Takeover time in evolutionary dynamic optimization: from theory to practice (2015)
  6. Das, Debasis; Roy, Arindam; Kar, Samarjit: A multi-warehouse partial backlogging inventory model for deteriorating items under inflation when a delay in payment is permissible (2015)
  7. Fichera, Sergio; Costa, Antonio; Cappadonna, Fulvio: Scheduling jobs families with learning effect on the setup (2015)
  8. Gupta, Sameer; Singla, Ekta: Evolutionary robotics in two decades: a review (2015)
  9. Kacprzyk, Janusz: Multistage fuzzy control of a stochastic system using a bacterial genetic algorithm (2015)
  10. Kostenko, V.A.; Frolov, A.V.: Self-learning genetic algorithm (2015)
  11. Li, Jinbo; Pedrycz, Witold; Wang, Xianmin: A rule-based development of incremental models (2015)
  12. Lim, Ting Yee; Al-Betar, Mohammed Azmi; Khader, Ahamad Tajudin: Adaptive pair bonds in genetic algorithm: an application to real-parameter optimization (2015)
  13. Padhye, Nikhil; Mittal, Pulkit; Deb, Kalyanmoy: Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization (2015)
  14. Pereira, André G.C.; de Andrade, Bernardo B.: On the genetic algorithm with adaptive mutation rate and selected statistical applications (2015)
  15. Purnomo, Hindriyanto Dwi; Wee, Hui-Ming: Soccer game optimization with substitute players (2015)
  16. Sangeetha, S.; Jeevananthan, S.: Influence of crossover methods used by genetic algorithm-based heuristic to solve the selective harmonic equations (SHE) in multi-level voltage source inverter (2015)
  17. Xu, Yulong; Fang, Jian-An; Zhu, Wu; Wang, Xiaopeng; Zhao, Lingdong: Differential evolution using a superior-inferior crossover scheme (2015)
  18. Alberto, Isolina; Coello Coello, Carlos A.; Mateo, Pedro M.: A comparative study of variation operators used for evolutionary multi-objective optimization (2014)
  19. Bueno, Paulo M.S.; Jino, Mario; Wong, W.Eric: Diversity oriented test data generation using metaheuristic search techniques (2014)
  20. Czarnowski, Ireneusz; Jȩdrzejowicz, Piotr: Designing RBF networks using the agent-based population learning algorithm (2014)

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