Abstract
The growing impact of climate change has highlighted the importance of effective disaster housing recovery (DHR) measures, particularly in resource-constrained places prone to flooding. As these communities confront displacement and financial instability, allocating resources for post-DHR is crucial. This study presents an innovative strategy for improving DHR planning and execution that uses genetic algorithms (GAs), with a focus on Long-Term Recovery Groups (LTRGs) and community engagement for long-term results. By utilizing adaptive capabilities of GAs, the model efficiently navigates the complexity of resource allocation, balancing several criteria, such as cost-effectiveness, housing coverage, and stakeholder needs. This study evaluates the efficacy of GAs in DHR planning by developing and evaluating hypotheses on optimization, LTRG preparedness, and community autonomy. The results show that GA-driven planning considerably improves resource allocation decisions, promoting resilience and long-term recovery. The findings highlight the ability of GAs to solve complex difficulties in DHR, providing insights for policymakers, urban planners, and disaster response teams looking to improve recovery processes and community -resilience.