Genetic Algorithms - Jeremy Fisher

Genetic Algorithms: Programming by the Seat of Your Genes!

The term Genetic Algorithms sounds intimidating to most, a subject obviously beyond the comprehension of anyone with fewer than two advanced degrees. But in truth, genetic algorithms are – like the biological evolution that inspired them – little more sophisticated than trial and error, and their power to solve problems with complex constraints makes them a tool worth having. This talk will bring genetic algorithms out of academic papers and expensive textbooks and teach those of us in industry what's needed to put them to use.

About the Speaker

Jeremy Fisher is Director of Advanced Engineering and Data Science in DST's Applied Analytics Group, where he leads a team of data hackers and algorithm junkies. Prior to joining DST, Mr. Fisher was a Group Technical Director at VML advancing brands like Gatorade and Revlon, and before that was Director of Software Engineering at Adknowledge, where he was responsible for the advertiser technology platform. His specialties are fast-paced engineering, internet-scale architectures, and leading the best and brightest engineers and scientists.

[stevecook]

For the question at 37:00 - The local optima problem is mostly addressed in GA with the mutation. The best mutation methods and factors help in "jumping out" of local optima.

[Not.So.WiseGuy]

Great talk. It felt weird when there was just silence after any of the jokes.

[ldandco]

From 4:58 to 5:40
I was looking for a simple explanation on GA. That was it. Simple
Most other explanations I've found over complicate things and make it feel like the foundation is more difficult than what it is

[distrologic]

What I am often missing in disscussions about genetic algorithms is the link to biology. Because what GAs do is harnessing darwinistic and evolutionary principles (obviously). I think there is a lot more from biology that can be applied to GAs, for example how the competition between solutions actually affects the selection and how heavier mutation or larger populations affect the generated solutions.

Also I feel like neural networks and genetic algorithms are the perfect fit for AI. One could use a genetic algorithm to evolve a neural network or have a neural network optimize a genetic algorithm by learning what mutations make the solutions score better. So basically a learning genetic algorithm.

[jomilojuodeyemi]

best talk/ tutorial i've encountered on the subject. I was stuck on the subject of encoding until this video.

[patmull]

Wow. This was really great. I like those nice visuals and color representations!

[gdolphy]

So far the GA and neural methods don't really mimick so much. One missing component is a morpholagicl neural network.

Not only do the weights change but the connections too.

The neurons should be input^input
And connections (input!/input).

Also on the out put methods remove any if statements and recode such that the neurons have full control of the out put.

[pyb.]

This feels like a practice in front of the speaker's cat instead of the actual presentation.

[DasAntiNaziBroetchen]

Wtf is wrong with the dude at 44:00 waving the microphone around while talking?? I can't hear SHIT!

[TheHpsh]

just wonder, has anyone tried to do something like the traveling salesman problem, with a genetic algorithm without crossover, in my head it would make more sense