From 2008.igem.org
Agent Based Systems
The idea of agent based systems or modeling first took place in the late 1940s. This kind of modeling puts forth the idea of having independent individuals or agents within a network that have their own actions and interactions, and then combining then, and seeing how it affects the system as a whole. Each agent follow simple rules that allow for changing interactions in varying conditions. With the simultaneous operations of each agent, this kind of simulation would in turn re-create or predict the actions of a complex phenomenon of the natural world.
An example of agent based systems can be seen in an ant and its colony. Individually, an ant carries out its own function that can be seen as simple by nature. However, when an ant is put together into its own colony, then a more complex system can be seen. Each ant is still carrying out its own function, but when combined together, there's an overlying grand picture or scheme that is at work.
In summary, agent based systems are a decentralized system where the overall behavior emerges from the individual simple rules each agent carries out. As a result, this kind of modeling is fit for simulating molecules and their interactions since any molecular system is decentralized in principle.
Evolutionary Algorithm
EvoGEM follows an evolutionary approach to iteratively improve a population of circuits through subtle changes to the individuals. By taking individual circuits, modifications can be made in order to create an optimal solution. This is done by applying Darwinian selection on the individuals, where each individual must meet a fitness criteria to survive to the next generation. If there is survival, then there will be random changes or adaptations made to the circuit that may or may not improve the fitness of the circuit. Overall, this kind of approach is based on the two principles of adaptation
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