Genocop

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.


References in zbMATH (referenced in 1099 articles )

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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. D’Angelo, Gianni; Palmieri, Francesco: GGA: a modified genetic algorithm with gradient-based local search for solving constrained optimization problems (2021)
  4. Garcia-Arca, Jesus; Comesaña-Benavides, Jose A.; Gonzalez-Portela Garrido, A. Trinidad; Prado-Prado, J. Carlos: Methodology for selecting packaging alternatives: an “action research” application in the industrial sector (2021)
  5. Khalaf, Lynda; Lin, Zhenjiang: Projection-based inference with particle swarm optimization (2021)
  6. Liagkouras, Konstantinos; Metaxiotis, Konstantinos: Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions (2021)
  7. Lopez-Sanchez, Misael; Cosío-León, M. A.; Martínez-Vargas, Anabel: Comparative analysis of constraint handling techniques based on Taguchi design of experiments (2021)
  8. 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)
  9. Miroforidis, Janusz: Bounds on efficient outcomes for large-scale cardinality-constrained Markowitz problems (2021)
  10. 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)
  11. Alharbi, Abir: A genetic-ELM neural network computational method for diagnosis of the Parkinson disease gait dataset (2020)
  12. Gómez, Miller Cerón; Yang, Hyun Mo: Mathematical model of the immune response to dengue virus (2020)
  13. Huang, Xuewen; Zhang, Xiaotong; Islam, Sardar M. N.; Vega-Mejía, Carlos A.: An enhanced genetic algorithm with an innovative encoding strategy for flexible job-shop scheduling with operation and processing flexibility (2020)
  14. Hussain, Abid; Cheema, Salman A.: A new selection operator for genetic algorithms that balances between premature convergence and population diversity (2020)
  15. Katsifarakis, Konstantinos L.; Kontos, Yiannis N.: Genetic algorithms: a mature bio-inspired optimization technique for difficult problems (2020)
  16. Liao, Yi; Diabat, Ali; Alzaman, Chaher; Zhang, Yiqiang: Modeling and heuristics for production time crashing in supply chain network design (2020)
  17. Oishi, Atsuya; Yagawa, Genki: A surface-to-surface contact search method enhanced by deep learning (2020)
  18. Pakhira, N.; Maiti, K.; Maiti, M.: Two-level supply chain for a deteriorating item with stock and promotional cost dependent demand under shortages (2020)
  19. Penev, Kalin: Precision in high dimensional optimisation of global tasks with unknown solutions (2020)
  20. Wang, Chun-feng; Liu, Kui; Shen, Pei-ping: A novel genetic algorithm for global optimization (2020)

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