  This study explores the integration of Lamarckian system into evolutionary
robotics (ER), comparing it with the traditional Darwinian model across various
environments. By adopting Lamarckian principles, where robots inherit learned
traits, alongside Darwinian learning without inheritance, we investigate
adaptation in dynamic settings. Our research, conducted in six distinct
environmental setups, demonstrates that Lamarckian systems outperform Darwinian
ones in adaptability and efficiency, particularly in challenging conditions.
Our analysis highlights the critical role of the interplay between controller
\& morphological evolution and environment adaptation, with parent-offspring
similarities and newborn \&survivors before and after learning providing
insights into the effectiveness of trait inheritance. Our findings suggest
Lamarckian principles could significantly advance autonomous system design,
highlighting the potential for more adaptable and robust robotic solutions in
complex, real-world applications. These theoretical insights were validated
using real physical robots, bridging the gap between simulation and practical
application.
