QMC SUITE OF PROGRAMS
Includes an evolutionary algorithm as an advanced add on
program
e Model
Provides actual equations!
An evolutionary process modeling tool that discovers data relationships between variables and a desired output. e provides the actual equations. As it solves problems, it learns from derived solutions, becoming smarter with experience. e may be applied to any engineering problem, control, numerical analysis, pattern recognition, etc. e will solve complex, scientific and engineering problems and return the solution in a straightforward, easy format.
This display shows the graphical interface illustrating the results of an e modeling session. The data is the radius and area of a circle. e model successfully found the equation which is area = p r2, where p is 3.14159 and r is radius. e Model has been successfully used for modeling systems with over nine variables and provides the actual equation to model the process. Results for a polymer plant process show a relative error and standard error deviation reduction of 50% when compared to neural networks using the same data for modeling, testing, and on-line prediction.
This display shows the graphical interface illustrating the results of an e modeling session. The e Model algorithm correctly fit the data for the equation y = minus the square root of x.
Fitness History: The Fitness History is based on the scores computed in past
evolutionary processes. The Fitness Graph displays the progress of each ecosystem.
Score Diversity: Scoring is used to determine the fitness of each entity in the ecosystem
population. The score diversity plot shows the distribution of scores in each population.
Truth and Prediction: The output
values are calculated from each row of data and then compared to the training table data.
"True" values are then plotted against "Calculated" values to display
the progress of each evolutionary pass.
Output Code: Derived
solutions are straightforward, understandable, and in a very useable format of pseudo
code, C, FORTRAN, or Basic.
What is an Evolutionary Algorithm? e uses a process modeled after biological evolution to evolve computer programs. Randomly generated populations of computer code which represent candidate solutions to your problem are evaluated for fitness and then selected to parent the next improved generation of programs. For example, use e to find a signal processing algorithm, to encapsulate a calibration table, to discover a securities buy/sell rule, or to evolve a decision algorithm.
Extensive evaluations made by QMC found e Model to be the highest quality program available. A link is provided to view the comparison results of three modeling approaches for a polymerization process.
Key Benefits
Minimizes preparation effort and optimizes your time
Provides the actual equations
No initial assumptions are needed
Use e whenever you need to discover the relationship (algorithm, model, control law, etc.) between variables and a desired output. e Model has been successfully used for modeling systems with over nine variables and provides the actual equation to model the process. Results for a polymer plant process show a relative error and standard error deviation reduction of 50% when compared to neural networks using the same data for modeling, testing, and on-line prediction.
Key Features
Model is evolved at solution time
Variety of solutions are explored
Computer software searches for solution
Solution is as good as empirical data collected
Solutions are easily adapted to changing requirements
Libraries of past learning are used for new problems.
Outputs to C, FORTRAN, or Basic
Use For
Pattern Recognition
Numerical Analysis
Sensor Fusion
Component Response
Economics
Engineering
Signal Processing
Mathematics
System Response
Control
QMC is an authorized distributor of e.
For additional information, give QMC a call at 281.359.4471 or visit the System
Dynamics International web site.