3. INTRODUCTION
Genetic Algorithms (GAs) were first introduced by John H. Holland in
the 1960s
Incorporate ideas of natural evolution
4. GENETIC ALGORITHM
Initial population created consisting randomly generated rules
Each rule represented by string of bits
5. GENETIC ALGORITHM
Let samples be described by two Boolean attributes: A1 and A2
C1 and C2 be the class
Rule: If A1 and not A2 then C2 can be encoded as bit string “100”; two
leftmost bits represents attributes A1 and A2 and rightmost bit represent
class
If not A1 and not A2 then C1 can be encoded as 001
If an attribute has k values, k>2, k bits may be used to encode attributes
values
6. GENETIC ALGORITHM
Based on notion of survival of fittest, new population is formed to consist of
fittest rules in current population, as well as offspring of these rules.
Fitness of rule is assesses by its classification accuracy on set of training
samples
Offspring created by applying genetic operators such as crossover and
mutation
Crossover substrings from pair of rules are swapped to form new pair rules
Mutation ,randomly selected bits in rule string are inverted
9. USES OF GENETIC ALGORITHM
Genetic algorithms are easily parallelizable
Used for classification
Optimization
Used to evaluate the fitness of other algorithm
10. REFERENCES
Data mining. concepts and techniques, 3rd edition(The Morgan
Kaufmann Series in Data Management System)
https://www.geeksforgeeks.org/genetic-algorithms/
https://www.researchgate.net/figure/Crossover-and-mutation-
operations-in-genetic-algorithm_fig2_245282272