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. Álvarez-Miranda, Eduardo; Sinnl, Markus: Exact and heuristic algorithms for the weighted total domination problem (2021)
  2. Benouhiba, Toufik: A multi-level refinement approach for structural synthesis of optimal probabilistic models (2021)
  3. Liagkouras, Konstantinos; Metaxiotis, Konstantinos: Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions (2021)
  4. Luo, Lan; Zhang, Zhe; Yin, Yong: Simulated annealing and genetic algorithm based method for a bi-level \textitseruloading problem with worker assignment in \textitseruproduction systems (2021)
  5. Yin, Jianan; Ma, Yuanyuan; Hu, Yuxin; Han, Ke; Yin, Suwan; Xie, Hua: Delay, throughput and emission tradeoffs in airport runway scheduling with uncertainty considerations (2021)
  6. Hussain, Abid; Cheema, Salman A.: A new selection operator for genetic algorithms that balances between premature convergence and population diversity (2020)
  7. Katsifarakis, Konstantinos L.; Kontos, Yiannis N.: Genetic algorithms: a mature bio-inspired optimization technique for difficult problems (2020)
  8. Liao, Yi; Diabat, Ali; Alzaman, Chaher; Zhang, Yiqiang: Modeling and heuristics for production time crashing in supply chain network design (2020)
  9. Oishi, Atsuya; Yagawa, Genki: A surface-to-surface contact search method enhanced by deep learning (2020)
  10. Pakhira, N.; Maiti, K.; Maiti, M.: Two-level supply chain for a deteriorating item with stock and promotional cost dependent demand under shortages (2020)
  11. Penev, Kalin: Precision in high dimensional optimisation of global tasks with unknown solutions (2020)
  12. Wang, Chun-feng; Liu, Kui; Shen, Pei-ping: A novel genetic algorithm for global optimization (2020)
  13. Ait Laamim, M.; Makrizi, A.; Essoufi, E. H.: Application of genetic algorithm for solving bilevel linear programming problems (2019)
  14. Coronel, Aníbal; Berres, Stefan; Lagos, Richard: Calibration of a sedimentation model through a continuous genetic algorithm (2019)
  15. Crawford, Broderick; Soto, Ricardo; Riquelme, Luis; Astorga, Gino; Johnson, Franklin; Cortés, Enrique; Castro, Carlos; Paredes, Fernando; Olivares, Rodrigo: A self-adaptive biogeography-based algorithm to solve the set covering problem (2019)
  16. Khakifirooz, Marzieh; Wu, Jei-Zheng; Fathi, Mahdi: Smart production by integrating product-mix planning and revenue management for semiconductor manufacturing (2019)
  17. Milewski, Sławomir: Determination of the truss static state by means of the combined FE/GA approach, on the basis of strain and displacement measurements (2019)
  18. Poczeta, Katarzyna; Kubuś, Łukasz; Yastrebov, Alexander: Structure optimization and learning of fuzzy cognitive map with the use of evolutionary algorithm and graph theory metrics (2019)
  19. Rodrigues, Filipe; Requejo, Cristina: Suppliers selection problem with quantity discounts and price changes: A heuristic approach (2019)
  20. Sambatti, Sabrina B. M.; de Campos Velho, Haroldo F.; Chiwiacowsky, Leonardo D.: Epidemic genetic algorithm for solving inverse problems: parallel algorithms (2019)

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