Warm-up Activity

1. How many frames are in a Pixar animated movie such as The Incredibles?

Genetic Algorithms:“Natural Selection”

Genetic Algorithms

HISTORY:1960s Evolutionary computing used to solve

complex engineering problems by Rechlenberg

1970s Genetic algorithms invented by John Holland

1980s GE begins selling first genetic algorithm product

1992 John Koza invents genetic programming

Genetic Algorithms

Genetic algorithms have lots of real world applications:

Automotive car design for composite materials and aerodynamics simultaneously

Genetic Algorithms

Genetic algorithms have lots of real world applications:

Engineering design of complex components, structures and operations (e.g. heat exchanger optimization, turbines, building trusses).

Genetic Algorithms

Genetic algorithms have lots of real world applications:

Evolvable Hardware - electronic circuits created by GA computer models that use stochastic (statistically random) operators to evolve new configurations from old ones.

Genetic Algorithms

Genetic algorithms have lots of real world applications:

Encryption and Code Breaking- GAs can be used both to create encryption for sensitive data as well as to break those codes

Genetic Algorithms

Genetic algorithms have lots of real world applications:

Molecular Design - GA optimization and analysis is used for designing industrial chemicals or for proteins used in pharmaceuticals.

Genetic Algorithms

Genetic algorithms have lots of real world applications:

Biomimetics - GA optimization and analysis is used in the development of technologies inspired by designs in nature.

Genetic Algorithms

Genetic algorithms have lots of real world applications:

Linguistics- GA can be used to generate puns or even help write jokes!

Genetic Algorithms

STRENGTHS:

• Good at finding solutions quickly• Capable of finding multiple solutions• Can solve problems that are not well

understood

Genetic Algorithms

WEAKNESSES:

• Doesn’t discriminate between local and global minimums

• No guarantee of finding the best solution; only returns “good” soluton

• Difficult to predict performance; requires a lot of fine tuning

Genetic Algorithms

Genetic algorithms usually consist of the following five steps:

1. Create a starting population randomly2. Test the fitness of each member and assign

selection probability3. Reproduce4. Test new population for threshold criteria5. Wash, rinse and repeat…

Genetic Algorithms

Reproduction:

– Select two parent chromosomes from a population according to their fitness)

– Cross over the parents to form a new offspring (children).

– Mutate new offspring at each locus (position in chromosome).

– Place new offspring in a new population

Genetic Algorithms

Now let’s put this to work…

X3 – Y2 + Z = 25

Let’s find a solution set [X,Y,Z] for this equation as a class by using a simple GA routine. You’ll need a pencil and maybe a calculator.