Understand genetic algorithms, their connection to Darwin's theory, and their natural selection as tools for solving thorny Python problems
Genetic algorithms are optimization techniques that build on the concepts of the theory of natural selection. They hold great promise for Python-users struggling with problems that involve matching, grouping, or optimizing on sequences. In this Oriole, open source maven Safia Abdalla covers the history of genetic algorithms, the implementation of a simple genetic algorithm, interesting use cases of genetic algorithms, and the future of the technique in academia and industry.
What you will learn:
- Explore a problem solving technique used by NASA radio designers and Wall Street stock selectors
- Survey the types of data science problems genetic algorithms solve and how they solve them
- Discover how to best structure a problem when using genetic algorithms as its problem solving tool
- Gain hands-on experience by using this tool to resolve a tricky (and funny) seating chart issue
- Learn why genetic algorithms are just algorithmic approximations of natural selection