Code for my academic research project titled "Optimizing Genetic Algorithm for Faster Convergence in Feature Selection"

Genetic algorithms (GA) are widely used for optimization problems due to their ability to explore the search space and find optimal solutions. However, the time required to evaluate the fitness function for every individual in the population can be a bottleneck and lead to slow convergence. To address this issue, we propose a novel approach to optimize the genetic algorithm for faster convergence. Our approach involves calculating the fitness function using batches of data, which significantly reduces the time required for each evaluation. We evaluate our approach on several benchmark problems and show that it leads to improvement in the speed of convergence and the overall efficiency of the genetic algorithm. The fitness functions are typically based on metric accuracy. The algorithm then applies genetic operators such as mutation and crossover to evolve the population of feature subsets. Our approach can be applied to a wide range of optimization problems and can help reduce the computational costs associated with the genetic algorithm.

• Load and Prepare datasets
• Divide data into appropriate size batches as per literature survey
• Calculate Fitness Score for Each Batch
• Calculate Fitness Score for Each Batch

This is baseline accuracy as a reference i.e. the accuracy of all should be around it

Note: Time is not negative ! That's a dash (-) without space

From all the results obtained from the conducted experiments, we have concluded the following:

1. The most efficient batch size for the larger dataset (with data more than 40k) is
2. This size may vary as the dataset size grows.
3. For the dataset of medium size (with data more than 10k and less than 30k) the best batch size seems to be working is 256. Again, the size of the batch may also vary as the size of dataset is at peak (around 30k or more). The size of 512 works better on upper bound size whereas the 256 works on lower bound size.
4. The proposed model shows very high inconsistency on smaller dataset (with data less than 2k – 3k). The dataset itself is so small that splitting it into smaller batches doesn’t show any significant effects on results.

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