title: Convergence of evolutionary algorithms on the n-dimensional continuous space.
abstract: Evolutionary algorithms (EAs) are random optimization methods inspired by genetics and natural selection, resembling simulated annealing. We develop a method that can be used to find a meaningful tradeoff between the difficulty of the analysis and the algorithms' efficiency. Since the case of a discrete search space has been studied extensively, we develop a new stochastic model for the continuous n-dimensional case. Our model uses renewal processes to find global convergence conditions. A second goal of the paper is the analytical estimation of the computation time of EA with uniform mutation inside the (hyper)-sphere of volume 1, minimizing a quadratic function.
authors.forename: Alexandru
authors.initials: A
authors.lastname: Agapie
authors.forename: Mircea
authors.initials: M
authors.lastname: Agapie
authors.forename: Gunter
authors.initials: G
authors.lastname: Rudolph
authors.forename: Gheorghita
authors.initials: G
authors.lastname: Zbaganu
mesh_terms: D000465:Algorithms; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D008957:Models, Genetic; D015233:Models, Statistical; D013269:Stochastic Processes