A Caltech Library Service

Neuro-Evolution Using Recombinational Algorithms and Embryogenesis for Robotic Control


Roy, Anthony Mathew (2010) Neuro-Evolution Using Recombinational Algorithms and Embryogenesis for Robotic Control. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/YNED-VN66.


Control tasks involving dramatic nonlinearities, such as decision making, can be challenging for classical design methods. However, autonomous, stochastic design methods such as evolutionary computation have proved effective. In particular, genetic algorithms that create designs via the application of recombinational rules are robust and highly scalable. Neuro-Evolution Using Recombinational Algorithms and Embryogenesis (NEURAE) is a genetic algorithm that creates C++ programs that in turn create neural networks which can function as logic gates. The neural networks created are scalable and robust enough to feature redundancies that allow the network to function despite internal failures. An analysis of NEURAE evinces how biologically inspired phenomena apply to simulated evolution. This allows for an optimization of NEURAE that enables it to create controllers for a simulated swarm of Khepera-inspired robots.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Genetic Algorithm, Neural Network, Robotics, Genetic Programming, Artificial Intelligence
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Mechanical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Antonsson, Erik K. (co-advisor)
  • Shapiro, Andrew A. (co-advisor)
  • Burdick, Joel Wakeman (advisor)
Thesis Committee:
  • Burdick, Joel Wakeman (chair)
  • Abu-Mostafa, Yaser S.
  • Daraio, Chiara
  • Antonsson, Erik K.
  • Shapiro, Andrew A.
Defense Date:11 December 2009
Record Number:CaltechTHESIS:06092010-140839602
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:5944
Deposited By: Anthony Roy
Deposited On:04 Aug 2010 17:57
Last Modified:08 Nov 2019 18:12

Thesis Files

PDF - Final Version
See Usage Policy.


Repository Staff Only: item control page