In this work we present our computational framework, named Evolution. It allows the user to study and explore complex problems, such as those from structural biology, by providing the means to experiment with the system and to develop functionalities and algorithms to adapt the construct to the singularities of the problem. The ensemble of this tool was achieved including the next strategies or approaches in its design:
1) evolutionary algorithms (EA);
2) multi-agent systems (MAS);
3) integration of the computational platform, with the advantages of having both, EAs, involved in a MAS;
4) selection of protein folding as the starting case of structural biology to be implemented;
5) take advantage of the already employed computational strategies (e.g. EA) for the protein folding problem;
6) adaptation of the Evolution platform to handle protein folding problems;
7) explore and test Evolution capabilities in simplistic protein folding experiments of ideal hydrophobic-polar (HP) sequences already reported in the literature in 2D/3D discrete lattices;
8) feedback to solve more complex structures and concluding analysis of the results.
We also present and describe the Evolution infrastructure in its version 1.0, emphasizing on its structural and functional characteristics, and discussing the first results obtained studying different 2D model systems in their most simple representation.