@article{(Open Science Index):https://publications.waset.org/pdf/1813,
	  title     = {Evolutionary Algorithms for Learning Primitive Fuzzy Behaviors and Behavior Coordination in Multi-Objective Optimization Problems},
	  author    = {Li Shoutao and  Gordon Lee},
	  country	= {},
	  institution	= {},
	  abstract     = {Evolutionary robotics is concerned with the design of
intelligent systems with life-like properties by means of simulated
evolution. Approaches in evolutionary robotics can be categorized
according to the control structures that represent the behavior and the
parameters of the controller that undergo adaptation. The basic idea
is to automatically synthesize behaviors that enable the robot to
perform useful tasks in complex environments. The evolutionary
algorithm searches through the space of parameterized controllers
that map sensory perceptions to control actions, thus realizing a
specific robotic behavior. Further, the evolutionary algorithm
maintains and improves a population of candidate behaviors by
means of selection, recombination and mutation. A fitness function
evaluates the performance of the resulting behavior according to the
robot-s task or mission. In this paper, the focus is in the use of
genetic algorithms to solve a multi-objective optimization problem
representing robot behaviors; in particular, the A-Compander Law is
employed in selecting the weight of each objective during the
optimization process. Results using an adaptive fitness function show
that this approach can efficiently react to complex tasks under
variable environments.},
	    journal   = {International Journal of Mechanical and Materials Engineering},
	  volume    = {6},
	  number    = {6},
	  year      = {2012},
	  pages     = {560 - 565},
	  ee        = {https://publications.waset.org/pdf/1813},
	  url   	= {https://publications.waset.org/vol/66},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 66, 2012},
	}