The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Adaptive Self-Organizing Organisms Using a Bio-Inspired Gene Regulatory Network Controller: For the Aggregation of Evolutionary Robots Under a Changing Environment
Abstract
This work has explored the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behavior. Using an Alife simulation framework that mimics a changing environment, we have shown that separating the static from the conditionally active part of the network contributes to a better adaptive behavior. This chapter describes the biologically inspired concept of GRNs to develop a distributed robot self-organizing approach. In particular, it shows that by using this approach, multiple swarm robots can aggregate to form a robotic organism that can adapt its configuration as a response to a dynamically changing environment. In addition, through the comparison of several different simulation experiments, the results illustrate the impact of evolutionary operators such as mutations and duplications on improving the adaptability of organisms.
Related Content
|
Rashmi Rani Samantaray, Zahira Tabassum, Abdul Azeez.
© 2024.
32 pages.
|
|
Sanjana Prasad, Deepashree Rajendra Prasad.
© 2024.
25 pages.
|
|
Deepak Varadam, Sahana P. Shankar, Aryan Bharadwaj, Tanvi Saxena, Sarthak Agrawal, Shraddha Dayananda.
© 2024.
24 pages.
|
|
Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma, Bhupendra Kumar, Sachin Chaudhary, Rajneesh Panwar.
© 2024.
29 pages.
|
|
Mrutyunjaya S. Hiremath, Rajashekhar C. Biradar.
© 2024.
30 pages.
|
|
C. L. Chayalakshmi, Mahabaleshwar S. Kakkasageri, Rajani S. Pujar, Nayana Hegde.
© 2024.
30 pages.
|
|
Amit Kumar Tyagi.
© 2024.
29 pages.
|
|
|