Evolutionary Computation is a branch of computation that is quite unique.  For one, it is not specific to any problem or task.  It is a framework for solving generic problems.   This offers interesting capacities for the design process where we are usually the ones iterating over a design decision in order to evaluate its effectiveness.  If we could abstract the forces which guide a design decision, then we could potentially utilize Evolutionary computation, and specifically Genetic Algorithms, to assist us in finding optimal solutions given a number of design criteria.

For this to work, we must know the design task very well.  It’s parameters must be well defined into a ‘solution space’ bound by the ranges of our parameters.  This is a space where good and bad solutions exist, and the Evolutionary Solver will search through it to find the best solutions.


Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions

Genetic Workflows: Evolving Solutions


Design Associativity  – MAA 2015-2016 Term 2

Student : Naitik Shah

 

Instructor: Luis E. Fraguada

Assistant: Rodrigo Aguirre