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 1044 articles )

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  1. 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)
  2. Szabó, Norbert Péter; Dobróka, Mihály: Series expansion-based genetic inversion of wireline logging data (2019)
  3. Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using (\ell_1)-penalized likelihood (2018)
  4. Dana Mazraeh, Hassan; Abbasi Molai, Ali: Resolution of nonlinear optimization problems subject to bipolar max-min fuzzy relation equation constraints using genetic algorithm (2018)
  5. Fahimnia, Behnam; Davarzani, Hoda; Eshragh, Ali: Planning of complex supply chains: a performance comparison of three meta-heuristic algorithms (2018)
  6. Gansterer, Margaretha; Hartl, Richard F.: Centralized bundle generation in auction-based collaborative transportation (2018)
  7. Nouri, Nouha; Ladhari, Talel: Evolutionary multiobjective optimization for the multi-machine flow shop scheduling problem under blocking (2018)
  8. Phuc, Phan Nguyen Ky; Yu, Vincent F.; Chou, Shuo-Yan; Tsao, Yu-Chung: Effects of dominance on operation policies in a two-stage supply chain in which market demands follow the bass diffusion model (2018)
  9. Rocholl, Jens; Mönch, Lars: Hybrid algorithms for the earliness-tardiness single-machine multiple orders per job scheduling problem with a common due date (2018)
  10. Szabó, Norbert Péter; Dobróka, Mihály: Exploratory factor analysis of wireline logs using a float-encoded genetic algorithm (2018)
  11. Wang, Jiquan; Cheng, Zhiwen; Ersoy, Okan K.; Zhang, Panli; Dai, Weiting; Dong, Zhigui: Improvement analysis and application of real-coded genetic algorithm for solving constrained optimization problems (2018)
  12. Yu, Chunlong; Semeraro, Quirico; Matta, Andrea: A genetic algorithm for the hybrid flow shop scheduling with unrelated machines and machine eligibility (2018)
  13. Zou, Pan; Rajora, Manik; Liang, Steven Y.: A new algorithm based on evolutionary computation for hierarchically coupled constraint optimization: methodology and application to assembly job-shop scheduling (2018)
  14. Atifi, K.; Balouki, Y.; Essoufi, El-H.; Khouiti, B.: Identifying initial condition in degenerate parabolic equation with singular potential (2017)
  15. Drzisga, D.; Gmeiner, B.; Rüde, U.; Scheichl, R.; Wohlmuth, B.: Scheduling massively parallel multigrid for multilevel Monte Carlo methods (2017)
  16. Jain, Ashish; Chaudhari, Narendra S.: An improved genetic algorithm for developing deterministic OTP key generator (2017)
  17. Jana, Dipak Kumar; Das, Barun: A two-storage multi-item inventory model with hybrid number and nested price discount via hybrid heuristic algorithm (2017)
  18. Jin, Yin-Fu; Yin, Zhen-Yu; Shen, Shui-Long; Zhang, Dong-Mei: A new hybrid real-coded genetic algorithm and its application to parameters identification of soils (2017)
  19. 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)
  20. 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)

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