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 1079 articles )

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  1. Katsifarakis, Konstantinos L.; Kontos, Yiannis N.: Genetic algorithms: a mature bio-inspired optimization technique for difficult problems (2020)
  2. Liao, Yi; Diabat, Ali; Alzaman, Chaher; Zhang, Yiqiang: Modeling and heuristics for production time crashing in supply chain network design (2020)
  3. Oishi, Atsuya; Yagawa, Genki: A surface-to-surface contact search method enhanced by deep learning (2020)
  4. Pakhira, N.; Maiti, K.; Maiti, M.: Two-level supply chain for a deteriorating item with stock and promotional cost dependent demand under shortages (2020)
  5. Penev, Kalin: Precision in high dimensional optimisation of global tasks with unknown solutions (2020)
  6. Wang, Chun-feng; Liu, Kui; Shen, Pei-ping: A novel genetic algorithm for global optimization (2020)
  7. Ait Laamim, M.; Makrizi, A.; Essoufi, E. H.: Application of genetic algorithm for solving bilevel linear programming problems (2019)
  8. 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)
  9. Khakifirooz, Marzieh; Wu, Jei-Zheng; Fathi, Mahdi: Smart production by integrating product-mix planning and revenue management for semiconductor manufacturing (2019)
  10. 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)
  11. Rodrigues, Filipe; Requejo, Cristina: Suppliers selection problem with quantity discounts and price changes: A heuristic approach (2019)
  12. Sambatti, Sabrina B. M.; de Campos Velho, Haroldo F.; Chiwiacowsky, Leonardo D.: Epidemic genetic algorithm for solving inverse problems: parallel algorithms (2019)
  13. Szabó, Norbert Péter; Dobróka, Mihály: Series expansion-based genetic inversion of wireline logging data (2019)
  14. Tang, Zhili; Zhang, Lianhe: A new Nash optimization method based on alternate elitist information exchange for multi-objective aerodynamic shape design (2019)
  15. Bhunia, Asoke Kumar; Biswas, Amiya; Shaikh, Ali Akbar: Extended nondominated sorting genetic algorithm (ENSGA-II) for multi-objective optimization problem in interval environment (2018)
  16. Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using (\ell_1)-penalized likelihood (2018)
  17. Dana Mazraeh, Hassan; Abbasi Molai, Ali: Resolution of nonlinear optimization problems subject to bipolar max-min fuzzy relation equation constraints using genetic algorithm (2018)
  18. El Yafrani, Mohamed; Ahiod, Belaïd: Efficiently solving the traveling thief problem using hill climbing and simulated annealing (2018)
  19. Fahimnia, Behnam; Davarzani, Hoda; Eshragh, Ali: Planning of complex supply chains: a performance comparison of three meta-heuristic algorithms (2018)
  20. Nouri, Nouha; Ladhari, Talel: Evolutionary multiobjective optimization for the multi-machine flow shop scheduling problem under blocking (2018)

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