THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART

1,296 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A survey on optimization metaheuristics (2013, methodology): “EAs have been widely applied with a good measure of success to combinatori al optimization problems [33,26], constrained optimization problems [55], Data Mining and Knowledge Discovery [97], etc.”
An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions (2017, methodology): “They can be roughly divided into penalty function methods, special representations and operators, repair methods, separation of objectives and constraints and hybrid methods (Coello (2002)).”
Genetic-algorithm-based optimal apportionment of reliability and redundancy under multiple objectives (2009, background): “Although conceptually simple, this approach is quite difficult to implement in practice because in most problems the exact location of the boundary between the feasible and infeasible regions is unknown a priori ( Coello and Carlos, 2002 ).”
Bio-inspired computation: Where we stand and what's next (2019, background): “When this is the case, many strategies to handle different types of constraints have been reported by the community [235, 410, 411, 412].”
A novel heuristic optimization method: charged system search (2010, background): “Rule 1 Many of the natural evolution algorithms maintain a population of solutions which are evolved through random alterations and selection [15]–[ 16 ].”