Scalability of the Bayesian optimization algorithm

TL;DR: The level-based theorem is presented, a new technique tailored to population-based processes where offspring are sampled independently from a distribution depending only on the current population that provides upper bounds on the expected time until the process reaches a target state.

TL;DR: The hierarchical Bayesian optimization algorithm (hBOA) as discussed by the authors solves nearly decomposable and hierarchical optimization problems scalably by combining concepts from evolutionary computation, machine learning and statistics.

TL;DR: This paper studies the computational time complexity of a simple EDA, i.e., the univariate marginal distribution algorithm (UMDA), and proves theoretically that the UMDA with margins can solve the BVLeadingOnes problem efficiently.

TL;DR: This paper reviews the topic of population sizing in genetic algorithms by revisiting theoretical models which rely on a facetwise decomposition of genetic algorithms, and then moves on to various self-adjusting population sizing schemes that have been proposed in the literature.

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.