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.