Organisms: A Genetic Algorithm for Nanoclusters
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How the Otago Research Genetic Algorithm for Nanoclusters, Including Structural Methods and Similatity (Organisms) Program Works
Installation: Setting Up the Organisms Program and Pre-Requisites Packages
How To Use The Organisms Program
Run.py
- Running the Genetic Algorithm
Examples of Running the Organisms Program with
Run.py
RunMinimisation.py
- Writing a Local Minimisation Function for the Genetic Algorithm
MakeTrials.py
- Creating Multiple, Repeated Genetic Algorithm Trials
Safely Finishing the Genetic Algorithm Midway through the Algorithm
Restarting the Genetic Algorithm
Common Issues of the Genetic Algorithm and Ways to Solve Them
Helpful Programs to Create and Run the Genetic Algorithm
Helpful Programs for Gathering data and Post-processing Data
Information about using the
make_energy_vs_similarity_results.py
script
Other Helpful Programs for Gathering data and Post-processing Data
Initialising a New Population
Using Predation Operators with the Genetic Algorithm
Using Fitness Operators with the Genetic Algorithm
The Structural Comparison Method (SCM)
Using the Memory Operator
Using Epoch Methods
Recording Clusters From The Genetic Algorithm
Using Databases with the Genetic Algorithm
Adding Surfaces
The Genetic Algorithm Python Files
Index
Python Module Index
Organisms: A Genetic Algorithm for Nanoclusters
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Initialising a New Population
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