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Evolution 3.0: Solve your everyday Problems with genetic Algorithms | Mey Beisaron
Solve everyday problems with genetic algorithms using Evolution 3.0. Learn how to generate an optimal college course timetable with constraints and witness the algorithm converge to a good solution.
- The concept of Evolution 3.0 is introduced as a way to solve everyday problems using genetic algorithms.
- The problem of generating an optimal timetable for college courses is used as an example, with constraints such as no clashes, minimum gap between classes, and limited time.
- The genetic algorithm is introduced, starting with the initial population of possible timetables, followed by fitness assessment, selection, crossover, and mutation.
- The fitness function is weighted to prioritize certain constraints, such as no clashes and limited time.
- The selection process uses a roulette wheel to choose parents for the next generation, with a focus on better solutions having a higher chance of being chosen.
- Mutation is introduced to insert random changes into the solutions, with a focus on minimizing clashes and gaps between classes.
- The algorithm is demonstrated to converge to a good solution after several generations, with the example timetable having no clashes and limited time.
- The concept of Evolution 3.0 is used to illustrate the process of solving problems using genetic algorithms.
- The algorithm is simple and can be used to solve real-world problems with ease.