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In some real-life optimization problems the objectives are often non-commensurable and are explicitly/mathematically not available.
Interactive Evolutionary Computation (IEC) can effectively handle these problems.
IEC uses human evaluation in the optimization system. Simply stated, IEC is a technique from the class of evolutionary algorithms (EAs), whose fitness function is replaced by a human. As in interactive evolution, the user selects one or more individual(s) which survive(s) and reproduce(s) (with variation) to constitute a new generation, IEC uses two different spaces for its search. Thehuman user evaluates the output of the target system according to the distance between the target goal and the system output in psychological space. On the other hand, the EA searches in the parameter space. It can be said that the IEC is the optimization technology where the EA and a human search are cooperatively based on the mapping between these two spaces.
More information, html help and three papers about the application of this toolbox are also available from here:
http://www.abonyilab.com/software-and-data/easy_iec
Citation pour cette source
Janos Abonyi (2026). Interactive Evolutionary Computing (EASY-IEC) MATLAB Toolbox (https://fr.mathworks.com/matlabcentral/fileexchange/47206-interactive-evolutionary-computing-easy-iec-matlab-toolbox), MATLAB Central File Exchange. Extrait(e) le .
Remerciements
A inspiré : Analyze and Visualize Earthquake Data in Python with Matplot
Informations générales
- Version 1.0.0.0 (932 ko)
Compatibilité avec les versions de MATLAB
- Compatible avec toutes les versions
Plateformes compatibles
- Windows
- macOS
- Linux
| Version | Publié le | Notes de version | Action |
|---|---|---|---|
| 1.0.0.0 |
