Project Abstract

The rationale for undertaking Fitness Landscape Analysis (FLA) is rooted in its capacity to reveal the fundamental structure and dynamics of intricate systems in several fields. FLA, which draws on topics including as evolutionary biology, optimization algorithms, and computational biology, provides valuable insights into the relationship between genotypes and reproductive success. This understanding is essential for studying evolutionary processes. FLA, or Fitness Landscape Analysis, is a valuable tool in optimization that helps in the development of more efficient algorithms. It achieves this by establishing a relationship between potential solutions and the values of the objective function. This analysis greatly improves problem-solving abilities in various fields, including engineering. Furthermore, in the field of computational biology, FLA reveals the intricate connections between the structure and function of proteins, providing valuable insights for the development of new drugs and the advancement of synthetic biology. Researchers acquire vital insights on optimizing systems, comprehending evolutionary processes, and designing creative solutions by exploring the ruggedness, neutrality, and epistasis of fitness landscapes. FLA is a powerful tool that can be used to analyze complex systems. It drives breakthroughs in various sectors by revealing the underlying landscapes of fitness and adaptation.

Keywords: optimization algorithms, problem solving, computational biology

 Conference Details

Session: Presentation Stream 23 at Presentation Slot 10

Location: College 127 at Wednesday 8th 09:00 – 12:30

Markers: Eike Neumann, Julian Hough

Course: MSc Data Science, Masters PG

Future Plans: I’m looking for work