Title :
Benchmarking Punctuated Anytime Learning for Evolving a Multi-Agent Team´s Binary Controllers
Author :
Blumenthal, H. Joseph ; Parker, Gary B.
Author_Institution :
George Mason Univ., Washington
Abstract :
Punctuated anytime learning (PAL) is a system that can be used for evolving cooperative teams of agents. PAL applied to evolving multiple populations is a kind of cooperative revolutionary algorithm (CCEA). Here we compare PAL to a canonical genetic algorithm (GA) on a widely used GA benchmarking optimization function called the Rosenbrock function. The Rosenbrock function was chosen for experimentation because it is a highly non-linear function and difficult to optimize. Results are shown from a variety of experiments with different dimensionalities of the Rosenbrock function. These findings are discussed in the context of evolving binary controllers for multi-agent cooperative teams of robots.
Keywords :
genetic algorithms; learning (artificial intelligence); multi-agent systems; multi-robot systems; Rosenbrock function; binary controller; cooperative revolutionary algorithm; cooperative teams of agents; genetic algorithm; multiagent cooperative teams of robots; punctuated anytime learning; robot control; Automatic control; Automation; Collaboration; Control systems; Educational institutions; Evolutionary computation; Genetic algorithms; Organizing; Robot control; Testing; Co-evolution; Evolutionary Algorithms; Learning; Optimization; Robot Control;
Conference_Titel :
Automation Congress, 2006. WAC '06. World
Conference_Location :
Budapest
Print_ISBN :
1-889335-33-9
DOI :
10.1109/WAC.2006.375997