genetic algorithms
genetic algorithms in title
genetic algorithms in description
Genetic algorithms are imlpemented as a computer simulation in which a population of abstract representations (called chromosomes or the genotype or the genome) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions.
Basic features of the library:
- Many genetic operations, chromosomes representation interfaces and different genetic algorithm.
- Easy customization of the library by using provided interfaces for genetic operations and chromosome representations.
- Support for multiple processors/cores with multithreaded execution of genetic algorithms.
Genetic Algorithm Viewer permits the user to test the major parameters of a genetic algorithm.
Physics, Biology, Economy or Sociology often have to deal with the classical problem of optimization. Economy particularly has become specialist of that field1. Generally speaking, a large part of mathematical development during the XVIIIth century dealt with that topic (remember those always repeated problems where you had to obtain the derivative of a function to find its extremes).
Purely analytical methods widely proved their efficiency. They nevertheless suffer from a insurmountable weakness : Reality rarely obeys to those wonderful differentiable functions your professors used to show you2.
Other methods, combining mathematical analysis and random search have appeared. Imagine you scatter small robots in a Mountainous landscape.
Those robots can follow the steepest path they found. When a robot reaches a peak, it claims that it has found the optimum. This method is very efficient, but theres no proof that the optimum has been found, each robot can be blocked in a local optimum. This type of method only works with reduced search spaces.
JOpt.NET will offer route and transport optimisation with respect to various constraints such as time windows, load capacities and pick up and delivery orders. The component is based on genetic algorithms and automatically determines an optimized allocation of vehicels to an arbitrary set of shipments.
· A modified Tudor Sorin version of backtracking
· A basic heuristic greedy algorithm
· A "forced" genetic algorithm, as I like to name it
· The .zip file also includes a sample program using this class
Beginning initially with random creatures, the screensaver uses a genetic algorithm to develop creatures capable of realistic locomotion behaviors.
The concept and algorithms implemented in breveCreatures are based on Karl Sims seminal work, "Evolved Virtual Creatures".
Because the screensaver uses an evolutionary algorithm, it may take a long timehours, or even daysbefore effective locomotion behaviors can be observed. Each time it is run, the screensaver will save its progress and continue from where it left off.
Features:
1. Any more layer numbers, any more node numbers of each layer and any transfer functions of each layer.
2. Visual design of network structures.
3. Visual, real time and dynamic control of learning process.
4: Can use multi-learning files with normal and grid data types.
5: Five learning algorithms, including genetic algorithms and Levnberg-Marquardt Algorithm. Fast converge of the learning can be easily achieved.
6: One-click settlement for time series analysis.
7: Steps-by-steps learning run mode.
8: Optimal analysis of inter-relationships and 2D-3D graphical evaluations of all factors.
10: Excel-like, MDI data file editor enhanced by VB, Java and Delphi/Pascal script languages. Input/export Excel (.xls), Lotus 1-2-3 (.wk1, .wks), QuarttorPro (.wq1), DBase File (.dbf), Access File (.mdb) and Comma-delimited Text File (.csv;.txt) directly.
11: Chart Function: create, edit, and store multi-charts within a single data file.
12: Linear, multi-linear regression with over 30 pre-defined functions.
13: Function/Equation fit (curve fit): fit any type of equation according to your data. No limitations on variables and parameters number
14: Equation solve: solve any type of equation or system equations by evolution algorithms (Genetic Algorithm, Particle Swarm Optimization and Rotation Inherit Optimization)
15: Matrix operation, data arrange tools, and much more...
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and much more
FindDec is a software for search of global and local optimums at system of functions with genetic algorithm. FindDec is available in three versions: Standard, Professional, and Unlimited. The number of variables in the FindDec professional must be no more than 150. FindDec solves maximum/minimum optimization problem in finance, distribution, scheduling, resource allocation, manufacturing, budgeting, engineering, and more. FindDec can find optimal solutions to problems which are "unsolvable" for standard linear and non-linear optimizers. FindDec can find optimal solutions to virtually any type of problem, from the simple to the most complex.
FindDec is useful and efficient when
* the method of an accurate decision does not know
* the method of an accurate decision knows, but it is very difficult to implement
* the exact optimum is not required, and the decision may be any value, which is more a set value
* the search space is large, complex or poorly understood
* traditional search methods fail
Work consists of six steps in the application FindDec
1. Input of the optimization problem
2. Option of parameters of genetic algorithm
3. Option of termination conditions of genetic algorithm
4. Option of output and saving
5. Searching of decisions
6. Saving of a result
Steps 1, 5, 6 are compulsory.
Requirements: 64 Mb Ram
FindDec is a software for search of global and local optimums at system of functions with genetic algorithm. FindDec is available in three versions: Standard, Professional, and Unlimited. The number of variables in the FindDec standard must be no more than 70. FindDec solves maximum/minimum optimization problem in finance, distribution, scheduling, resource allocation, manufacturing, budgeting, engineering, and more. FindDec can find optimal solutions to problems which are "unsolvable" for standard linear and non-linear optimizers. FindDec can find optimal solutions to virtually any type of problem, from the simple to the most complex.
FindDec is useful and efficient when
* the method of an accurate decision does not know
* the method of an accurate decision knows, but it is very difficult to implement
* the exact optimum is not required, and the decision may be any value, which is more a set value
* the search space is large, complex or poorly understood
* traditional search methods fail
Work consists of six steps in the application FindDec
1. Input of the optimization problem
2. Option of parameters of genetic algorithm
3. Option of termination conditions of genetic algorithm
4. Option of output and saving
5. Searching of decisions
6. Saving of a result
Steps 1, 5, 6 are compulsory.
Requirements: 64 Mb Ram
FindDec is a software for search of global and local optimums at system of functions with genetic algorithm. FindDec is available in three versions: Standard, Professional, and Unlimited. The number of variables in the FindDec Unlimited is not limited. FindDec solves maximum/minimum optimization problem in finance, distribution, scheduling, resource allocation, manufacturing, budgeting, engineering, and more. FindDec can find optimal solutions to problems which are "unsolvable" for standard linear and non-linear optimizers. FindDec can find optimal solutions to virtually any type of problem, from the simple to the most complex.
FindDec is useful and efficient when
* the method of an accurate decision does not know
* the method of an accurate decision knows, but it is very difficult to implement
* the exact optimum is not required, and the decision may be any value, which is more a set value
* the search space is large, complex or poorly understood
* traditional search methods fail
Work consists of six steps in the application FindDec
1. Input of the optimization problem
2. Option of parameters of genetic algorithm
3. Option of termination conditions of genetic algorithm
4. Option of output and saving
5. Searching of decisions
6. Saving of a result
Steps 1, 5, 6 are compulsory.
Requirements: 64 Mb Ram
genetic algorithms in software introduction
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