  Materialized views can significantly improve database query performance but
identifying the optimal set of views to materialize is challenging. Prior work
on automating and optimizing materialized view selection has limitations in
execution time and total cost. In this paper, we present a novel genetic
algorithm based approach to materialized view selection that aims to minimize
execution time and total cost. Our technique encodes materialized view
configurations as chromosomes and evolves the population over generations to
discover high quality solutions. We employ an adaptive mutation rate,
multi-objective fitness function, and lexicase selection to enhance genetic
search. Comprehensive experiments on the TPC-H benchmark demonstrate the
effectiveness of our algorithm. Compared to stateof-the-art methods, our
approach improves average execution time by 11% and reduces total materialized
view costs by an average of 16 million. These gains highlight the benefits of a
datadriven evolutionary approach. Our genetic algorithm framework significantly
outperforms current materialized view selection techniques in both efficiency
and total cost reduction. This work represents an important advance in enabling
performant and cost-effective utilization of materialized views in enterprise
systems.
