From 2008.igem.org
Agent Based Systems
Agent-based systems and modeling first emerged in the late 1940s. In this type of modeling, independent individuals or agents within a network that have their own actions and interactions. When these are combined, the observer sees how the system is affected 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 phenomena 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. However, when an ant is emerged into its own colony, then it combines to form a more complex system. Each ant still carries out its own function, but when combined together, there is an overlying grand picture that is at work.
In summary, agent-based systems are decentralized systems, where the overall behavior emerges from each agent's individual behaviour. 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 and selection.
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