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Genetic Algorithms (GA)
- Genetic Algorithm: based on an analogy to biological evolution
- An initial population is created consisting of randomly generated rules
- Each rule is represented by a string of bits
- E.g., if A1 and ¬A2 then C2 can be encoded as 100
- If an attribute has k > 2 values, k bits can be used
- Based on the notion of survival of the fittest, a new population is formed to consist of the fittest rules and their offspring
- The fitness of a rule is represented by its classification accuracy on a set of training examples
- Offspring are generated by crossover and mutation
- The process continues until a population P evolves when each rule in P satisfies a prespecified threshold
- Slow but easily parallelizable
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