# Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm
## 3 Proposed Methodology
### Processing Phase  
A Genetic Algorithm (GA) [40; 41; 42] is a randomized search technique that runs a function optimizer to explore and present a globally optimal solution for an optimization problem. GA search space parameters can be encoded as strings of binary digits, 0s and 1s, also called chromosomes. GA transforms the initial population of individual chromosomes into a new population based upon the chromosome fitness value and reproduction operators -- crossover and mutation. In this paper, ATS features are encoded into chromosomes, and then GA searches for the selection of appropriate features. The crossover and mutation operators accelerate the convergence of GA. The GA process involving Chromosome Selection, Simulating Binary Crossover, and Polynomial Mutation is continued, either for a specified number of generations or until the termination condition is achieved. In total, 8 features are generated in the former feature extraction phase for automatic text summarization for the Hindi language. The generated features are real values that lie in the range of 0 to 1 and decision variable/generated features need to be used directly. Hence, a real-coded genetic algorithm is requisite instead of a binary-coded genetic algorithm. In the case of RCGA, naive crossover and single crossover might fail. Additionally, the mutation strategy needs modification. * String Representation: In this research, Chromosome size \(C\) (=8) represents the total number of features or length of a chromosome. Each chromosome is a combination of 8 computed feature values. For a specific sentence, each value is in the range of 0 to 1. Based on this fact, consider the chromosome value at \(i\) = 3 position is 0.37 is the length of sentence (\(F_{ls}\)) feature value. So, each feature value for a specific sentence participates in the text summarization process. In a given population, all its chromosomes are initialized in the same manner. Table 4 exemplifies a chromosome with real coded values of 8 features --Sentence Paragraph Position (\(F_{sp}\): 0.40), Numerical Data (\(F_{nd}\): 0.39), Length of Sentence (\(F_{ls}\): 0.43), Keyword (\(F_{kn}\): 0.76), Sentence Similarity (\(F_{ss}\): 0.21), Named Entity (\(F_{n\epsilon}\): 0.34), English-Hindi words (\(F_{\epsilon h}\): 0.73), and Term Frequency-Inverse Document Frequency (\(F_{tl}\): 0.81). It is used for classifier construction. * Fitness Function: In the genetic algorithm, the fitness function is a unit measure that determines a chromosome that leads to the best solution among a pool of chromosomes and, hence, has its chance to be chosen as the next generation chromosome. In this research, for each input, maximum value of each \(C\) is 1 and minimum is 0 where \(C\) represents a number of features, and each \(C\) is a real value. The topmost chromosome having the highest recall is selected, and the fitness function is defined using Equation (10). \[F(s)=\sum_{j=1}^{C}f_{j}(s)\] (10) where \(f_{j}(s)\): \(j\)th feature of the sentence; \(C\): total number of features; \(F\): fitness function. * Selection of Best Chromosome: The selection operator determines which individual chromosomes can survive and continue to the next generation. In this research, the top two chromosomes are chosen as parents for the new generation since they give the highest recall measure through the fitness function. For the selection of parents, the most frequently used selection method -- the roulette wheel method [66] is chosen, which gives a chance to all the chromosomes without rejecting any of them. In this selection strategy, the whole population is partitioned through several individuals, where each sector of the roulette wheel represents an individual. The proportion of individual fitness to total fitness of the entire population decides -- the area of the sector for the individual and the probability of the individual being selected for the next generation.