Multi-operator based evolutionary algorithms for solving constrained optimization problems

TL;DR: A DE algorithm is proposed that uses a new mechanism to dynamically select the best performing combinations of parameters for a problem during the course of a single run and shows better performance over the state-of-the-art algorithms.

TL;DR: A survey on the use of ensemble strategies in POAs is provided and an overview of similar methods in the literature such as hyper-heuristics, island models, adaptive operator selection, etc. are provided and compare them with the ensemble Strategies in the context of POAs.

TL;DR: An analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms and the most popular approaches are analyzed in more detail.

TL;DR: This work aims to survey the most recent literature on home energy management systems, providing an aggregated and unified perspective in the context of residential buildings.

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 article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.

TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.