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README.md

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 # TFG-Cooperative-Coevolutionary-Algorithms
 Final project for a double degree in Computer Science and Mathematics. Covers introductory and advanced topics on evolutionary algorithms and CCEAs.
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+Provided code implements an experiment comparing performance of an evolutionary algorithm against its cooperative coevolutionary adaptation on optimizing a Boltzann machine. The weights of this network are predefined in a way that problem decomposition can be exploited. In order to replicate such experiments, the next steps must be followed:
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+  1. Compile the executables with the make command.
+  2. Run the dependency_extraction exe to generate the weights and read the dependencies into a file.
+  3. Run the code in the dependency_analysis Jupyter Notebook to group the variables into clusters and generate a .txt file that will be read by the CCEA algorithm in the first experiment.
+  4. Now you can execute the CCEAvsEA program. CCEAvsEA_2 can be executed right after compilation, since it generates its own weights assuming a specific variable clustering as domain knowledge.
+  5. Both, CCEAvsEA and CCEAvsEA_2 can be easily modified to read the previously generated weights (in case of CCEAvsEA_2, by following the indications on the comments) and repeat the experiment as many times as desired.
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+The attached report is written in Spanish with an extended summary in English at the beginning. For any question on the topics of this project or CCEAs, issues are accepted.