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Genetic Algorithm
 What is a genetic algorithm?
 “Genetic Algorithms
are defined as global
optimization procedures that use an
analogy of genetic evolution of biological
organisms.”
 Basically genetic algorithms tend to find
the best solution to a problem by following
an evolutionary process.
Basic Genetic Algorithm
Parallel Genetic Algorithm
 For large population sizes, G.A. is
computationally infeasible.
 Hence the use of Parallel Genetic
Algorithms.
Parallel Genetic Algorithm
Model and Encoding
 Island Model -: Each processor runs a
G.A. on a subset of the population and
there is periodic migration.
 Fixed Length Binary String Encoding -:
Here if gene is included in the subset
then value is 1 else 0.
Fitness Evaluation
 Two Different Criteria
 Classification Accuracy
 Size
of the subset
fitness(x) = w1 * accuracy(x) + w2 *(1 – dimensionality(x))
 Here,


accuracy(x) =test accuracy of the classifier
built with the gene subset represented by x
dimensionality(x)  [0,1] = the dimension
of the subset
Fitness Evaluation

w1 = weight assigned to accuracy

w2 = weight assigned to dimensionality
 High classification accuracy and low
dimension has high fitness.
Data Sets Used
Test Parameters
 The tests were run on two processors.
 The parameters of G.A. in each
processor were set as -:
 Population
Size : 1000
 Trials : 400000
 Crossover probability: 0.6
 Mutation probability: 0.001
Test Parameters

Selection Strategy: Elitist
 Migration Probability: 0.002
 Crossover probability of average level to get different
subpopulation with good traits of the parents.
 Mutation Probability low to avoid randomness of
selection.
 Selection Strategy is Elitist which ensures that the
best individuals are kept and hence leads to more
accurate subsets of genes.
Results
Results
 Leukemia Data Set
 Subset
with 29 Genes found
 Classifies 36/38 training instances correctly
 Classifies 30/34 test instances correctly
 Colon Data Set
 Subset
with 30 genes found
 92% accuracy on the training data set
Results Comparison
 Results better than other algorithms
such as G-S and NB algorithms which
have accuracies less than 90% and
gene numbers varying from 10 to 500.
Average Performance Graphs
Conclusion
 Method does well in finding smaller
gene subsets and better accuracies.
 Fitness function needs to be something
more sophisticated than the simple one
used right now to ensure a final
compact subset every time.