CODEWITHSUNDEEP
neural-networkgenetic-algorithmevolutionary-algorithmfunction-approximation
Johan
Johan

Reputation: 899

Difference between function approximator and optimization algorithm?

I just started learning about artificial neural networks and genetic algorithms and found that the difference between them is that ANN is a function approximator and that GA is an optimization algorithm (according to SO). Problem is I am not 100% sure where and how to draw the line between these definitions; is there a simpler way to explain what the difference is using e.g. analogies (assume I am a 10 year old)? What I found particularly confusing is that both types seem to be able to solve the same problem in some cases (e.g. Traveling Salesman Problem).

Upvotes: 0

Views: 307

Answers (1)

Francisco Yepes Barrera
Francisco Yepes Barrera

Reputation: 156

ANNs approximate an unknown function that correlates input and output. The goal of ANNs is to find the mathematical relation between both: if is presented a new input, the modeling found by the net gives an approximation to the true value. Example: find the pressure of gas in a tube giving as input temperature, viscosity, density, section of tube, ecc., using a set of measurements for training.

GAs are used often to find max or min of a function (optimization). For example: find the optimal net (minor error) for my previous example, using a set of nets, or solve the traveling salesman problem (given a set of cities, visit each city once and find the minimal path).

Upvotes: 1

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