Evolutionary Algorithm

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Agent Based Systems

Agent-based systems and modeling first emerged in the late 1940s. In this type of modeling, independent individuals or agents form a network, each having its own actions and interactions. When these are combined into a decentralized system, the observer sees how the system is affected as a whole. Each agent follows 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, EvoGEM makes modifications in order to create an optimal solution. It does this by applying Darwinian selection on the individuals, whereby each individual must meet a fitness criteria to survive to the next generation. If the individual survives, then there are random changes or adaptations made to the circuit, potentially improving the fitness of the circuit. Overall, this kind of approach is based on the two principles of adaptation and selection.

The evolutionary algorithm works in the following manner.

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-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 into a decentralized system, the observer sees how the system is affected as a whole. Each agent follows 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.
+Agent-based systems and modeling first emerged in the late 1940s. In this type of modeling, independent individuals or agents form a network, each having its own actions and interactions. When these are combined into a decentralized system, the observer sees how the system is affected as a whole. Each agent follows 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.
 [[Image:Agent based systems.PNG|thumb|left|150px]] 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.