Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms

TL;DR: In this article, the authors link entropy to estimation of distribution algorithms and propose a parallel island model for the quadratic assignment problem and a hybrid Cooperative Search Evolutionary Algorithm.

TL;DR: This work links entropy-based Convergence Measurement in Discrete Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem and learns Linguistic Fuzzy Rules by Using Estimation Of Distribution Algorithms as the Search Engine in the COR Methodology.

TL;DR: This work proposes customized model ensembles on demand, inspired by Lazy Learning, which finds the most relevant models from a DB of models, using their meta-information, and creates an ensemble, which produces an output that is a weighted interpolation or extrapolation of the outputs of the models ensemble.

TL;DR: This chapter aims to address some of the fundamental issues that are often encountered in optimization problems, making them difficult to solve, and to help both practitioners and fellow researchers to create more efficient optimization applications and novel algorithms.

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: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.

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.