Title :
Convergence of evolutionary algorithms in general search spaces
Author_Institution :
Informatik Centrum Dortmund, Germany
Abstract :
This paper provides conditions under which evolutionary algorithms with an elitist selection rule will converge to the global optimum of some function whose domain may be an arbitrary space. These results generalize the previously developed convergence theory for binary and Euclidean search spaces to general search spaces
Keywords :
Markov processes; convergence; genetic algorithms; probability; search problems; Euclidean search spaces; Markov model; arbitrary space; binary search; convergence theory; elitist selection rule; evolutionary algorithm convergence; function; general search spaces; global optimum; Algorithm design and analysis; Convergence; Design optimization; Evolution (biology); Evolutionary computation; Extraterrestrial measurements; Particle measurements; Random variables; State-space methods; Stochastic processes;
Conference_Titel :
Evolutionary Computation, 1996., Proceedings of IEEE International Conference on
Conference_Location :
Nagoya
Print_ISBN :
0-7803-2902-3
DOI :
10.1109/ICEC.1996.542332
