Title :
Environmental fitness for sustained population dynamics
Author_Institution :
Dept. of Comput. Sci. & Software Eng., Concordia Univ., Montreal, Que.
Abstract :
This study introduces an environment in which agents evolve freely, without an explicit fitness function. The evolution is mainly directed by environmental conditions, and their coupling with the agents. The goal consists in identifying preconditions for a sustainable environment allowing diversity. A method is proposed to analyze which configurations lead to rich population dynamics. The system global behavior is then tuned accordingly. Visualization is also a key component of this project: it offers a qualitative understanding of the simulations
Keywords :
genetic algorithms; multi-agent systems; predator-prey systems; agent evolution; environmental fitness; explicit fitness function; sustained population dynamics; Artificial intelligence; Computer science; Evolution (biology); Extraterrestrial phenomena; Genetic algorithms; Machine learning; Physics; Reverse engineering; Software engineering; Visualization;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Conference_Location :
Edinburgh, Scotland
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1554704
