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1
Introduction
2
Constraint Satisfaction
3
Evolutionary Computation
4
Genetic Algorithms
5
Generating the Population
6
Fitness Function
7
Gap Between Classes
8
Clashes
9
Selection
10
Frog Game
11
Implementation
12
Summary
Description:
Discover how to apply genetic algorithms to solve everyday problems in this 44-minute conference talk from MLCon. Explore the practical applications of evolutionary computation as speaker Mey Beisaron demonstrates coding a genetic algorithm from scratch to generate weekly schedules and create smart diet plans. Learn about the different stages of genetic algorithms, including population generation, fitness functions, selection processes, and implementation techniques. Gain insights into constraint satisfaction and see how concepts like the "Frog Game" can be applied to optimize solutions. By the end of this talk, acquire the knowledge to leverage genetic algorithms for tackling personal challenges and enhancing daily life efficiency.

Evolution 3.0 - Solve Your Everyday Problems with Genetic Algorithms

MLCon | Machine Learning Conference
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