- Knapsack Problem
- examples
of discrete optimization using heuristics
- 1-GMJ1,
an old version, Java 1
- 2-GMJ1
, automatic data path, Java 1- #1
- GMC ,
a C++ version
- an
example of cross-over rate optimization using genetic heuristics
- GMJ1,
optimizing a single parameter, Java 1
- GMJ2,
optimizing two parameters, Java 2- #1
- Flow-shop Problem
- GMC ,
an example of optimal scheduling using heuristics
- GMJ2 ,
an example of flow-shop scheduling by heuristics, no GMJ
- GMJ2 ,
an example of flow-shop scheduling by heuristics, updated no GMJ - #3
- GMJ2 ,
an example of flow-shop scheduling by heuristics, with GMJ - #2
- GMJ2 ,
an example of flow-shop scheduling by heuristics, signed with GMJ - #1
- Traveling Salesman Problem, examples of discrete
optimization
- Connecting Points by Flash 6.0
- Optimal Cutting Problem Using Java, examples of discrete
optimization using approximate algorithm
- GIILIOTINA-1,
without optimization of heuristic parameters, Java 1
- GIILIOTINA-2,
without optimization of parameters, Java 2
- GIILIOTINA-GMJ2,
with optimization of parameters by GMJ2 #2
- Ball Packer,
Heuristic 3d ball packer without parameter optimization
- Disc Packer,
Heuristic 2d disc packer without parameter optimization by Fortran
- Container
Packer, heuristic 3d container packer without parameter
optimization
- Container
Packer-2, heuristic 3d container packer with parameter
optimization- #3
- Container
Packer-signed, heuristic 3d container packer with parameter
optimization-signed #2
- Container
Packer-signed, updated heuristic 3d container packer with parameter
optimization-signed #1
- Cards Packer,
heuristic 2d server cards packer with parameter optimization
- School Scheduling Problem
- ISI
Records
- examples
of optimal scheduling using heuristics
- GMJ,
Automatic Correction of Data with Warnings and Various Tools for
Loading Files, Includes Other Examples
- WebStart-2,
Profiled school, penalty functions- #1
- WebStart,
Profiled school, penalty functions- #2
- GMJ12,
Profiled school, penalty functions- #3
- GMJ1,
Profiled school, penalty functions, xml i/o- #3
- INIT-2,
Profiled school, Initial Schedule-2
- INIT-XML,
Profiled school, Initial Schedule-XML
- GMJ1,
loading data to and from a user by CGI, updated
- Discussions-MIMOSA-GMJ1,
discussing applications of the scheduling system MIMOSA and the
optimization system GMJ1
- About-aSc,
discussing applications of the scheduling system aSc
Optimal Marriage Time Problem
- examples
of optimal sequential statistical decisions using dynamic programming
- Considering
Many Marriages
- "Buy-a-PC"
- 3*Bride-Multi
- an
Expert System of Multi-Bride Problem
- "Buy-an-Apartment":
an Application of "Multi-Marriage" Bride's Problem
Optimal RAM Ordering Considering Uncertain Future Demand,
Inventories Optimization by Expert System
Optimal Purchasing Time Problem, an example of optimal
sequential statistical decisions
Optimal Employment Problem, examples of optimal sequential
statistical decisions
Optimal Inspection Problem, example of game optimization
Bimatrix Game Problem
Game Scheduler , JavaWebStart example arranging schedule of games by SA
Shapley vector
Optimal Protection Problem
Optimal Diet Problem, examples of linear programming using
- Dieta-Doviltis,
Including ,Taste and Beauty Factors, corrected version
- Dieta-Simaitis,
Including Taste, Beauty and Diversity (by 0-1) Factors, alpha
version
- Dieta-Rasa-3,
Including Taste, Beauty and Diversity (by 0-1) Factors beta version, #5
- Dieta-Guogis,
Including Taste, Beauty and diversity (by 0-1-2-3) Factors, test version, #5
- Weekly Dieta-Egle,
Including Taste, Beauty and diversity (0-1-2) Factors, next test version, #4
- Weekly Dieta-Saulius,
Including Taste, Beauty and diversity (0-1-2) Factors, with servlet version, #1
- Weekly Dieta-Saulius,
Including Taste, Beauty and diversity (0-1-2) Factors, last test version, #2
- Weekly Dieta-Simas,
Including Taste, Beauty and diversity (0-1-2) Factors, EN/LT version, #3
- Dieta-Sprainyte,
using xml data
- Dieta-5.5
using Java and C++ libraries
- Diet-LPC using C
Best Mobile Plans, an example of vector optimization using
Pareto optimum