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![while True:
stack = population.sort(key=fitness) # Sort by fitness
fittest = stack[n_cull:] # Delete the weakest
population = []
for i in range(n_cull):
# Spawn mutated copies
population.append(random.choice(fittest).mutate())
population.extend(fittest)
in 7 lines of python:](https://image.slidesharecdn.com/2018-06-24geneticalgorithms-180601080914/85/An-introduction-to-Genetic-Algorithms-Dublin-Data-Science-15-320.jpg)
Uploaded byDavid Mack
An introduction to Genetic Algorithms (Dublin Data Science)
The document discusses the application of genetic algorithms in optimizing a recommendation system, particularly addressing challenges with hyper-parameter tuning. Genetic algorithms evolve a population by removing the weakest candidates and spawning mutated copies, allowing them to explore complex state spaces. While effective and versatile, these algorithms are time-intensive and require careful design considerations for implementation.
![Genetic Algorithm: A Crisp And Concise Introduction [Shakeeb A.]](https://cdn.slidesharecdn.com/ss_thumbnails/119m0027geneticalgo-200921155612-thumbnail.jpg?width=640&height=640&fit=bounds)
An introduction to Genetic Algorithms (Dublin Data Science)
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- 4. Failing to traina recommendation system Test accuracy Time 16%
- 5. Recommender Learned embedding Lots of hyper-parameters Embeddingsize Learning rate Number of deep layers etc....
- 6. Picking an embedding size 0.01 ? 0.03? 0.05 ? 1.0 ? 10.0 ? Help!!
- 7. So let’s trya genetic algorithm!
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- 11. x1 x2 x3x4 x5 x6 x7 x8 We create a population...
- 12. 1. Remove theweakest 2. Spawn mutated copies Repeat many times!
- 13. This two-step algorithmis able to maximise f(x)
- 14. The population fans-outover time DeepMind: Population based training of neural networks
- 15. while True: stack =population.sort(key=fitness) # Sort by fitness fittest = stack[n_cull:] # Delete the weakest population = [] for i in range(n_cull): # Spawn mutated copies population.append(random.choice(fittest).mutate()) population.extend(fittest) in 7 lines of python:
- 16. Benefits of geneticalgorithms
- 17. They can beapplied to diverse problems Neural networks antenna Race car humans
- 18. They often comeup with designs humans wouldn’t have thought of
- 19. They can navigatelarge and complex state spaces
- 20. They don’t requirefunctions to be differentiable
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- 22. They take alot of time/compute 6 million years =
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- 25. Designing your geneticalgorithm • Asexual vs sexual breeding • Breed top performers or all? • How often to cull? • Mutation rate
- 26. Bonus: Population basedtraining • Lets combine gradient descent with genetic algorithms for fun and profit • Good efficiency trade-off between trying many options and not discarding work done diagram
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- 30. Genetic algorithm vsGradient descent Genetic algorithm Gradient descent Works for any function Function must be differentiable Slower Faster Requires you to design a random mutation function Good for generating answers to “unknown” spaces Good for generating answers to well modelled spaces Very easy to distribute Harder to distribute
- 31. They help escapeyour “God complex”