Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art

By C. Coello-Coello. 2002

1,219 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ec0434534d9e80ac984f061d0fef193f79d284ff

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Citations in the corpus

A simple multimembered evolution strategy to solve constrained optimization problems (2005, background): “This has motivated the development of a considerable number of approaches to incorporate constraints into the fitness function of an EA [6], [25].”
Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites (2006, background): “There are many studies on solving constrained optimization problems using evolutionary algorithms (EAs) [1], [2].”
Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization (2010, background): “Consequently, a variety of EA-based constraint-handling techniques have been proposed for real-parameter optimization problems which can be grouped as [1]: (1) penalty functions; (2) special representations and operators; (3) repair algorithms; (4) separation of objectives and constraints; (5) hybri”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “from the methods included in both surveys [7,8] is the following:”
Penalty Function Methods for Constrained Optimization with Genetic Algorithms (2005, methodology): “Because finding a feasible solution in many GAs problems is a NP- hard itself.”
A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, background): “Since constraints can be handled using penalty functions [47], surrogates are built to approximate the penalty function instead of the individual constraint functions [48], [49].”
A CHARGED SYSTEM SEARCH WITH A FLY TO BOUNDARY METHOD FOR DISCRETE OPTIMUM DESIGN OF TRUSS STRUCTURES (2010, methodology): “In this method, the idea is to avoid the combination of the value of the objective function and the constraints of a problem to assign fitness, like when using a penalty function [19,21].”
Insights on Transfer Optimization: Because Experience is the Best Teacher (2018, background): “spirit of multi-fidelity optimization [33]), deciding the number of control parameters needed [34], constrained/unconstrained formulations [35], etc.”
Constraint-handling in genetic algorithms through the use of dominance-based tournament selection (2002, background): “However, despite the considerable number of constraint-handling methods that have been developed GAs in the last few years (see for example [24] [8]), most of them either require a large number of fitness function evaluations, complex encodings or mappings, or are limited to problems with certain…”
Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications (2017, background): “There are diferent types of penalty functions to handle constraint problems [8] :”
Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces (2019, background): “…algorithms (CMOEAs) are a promising way to deal with CMOPs [10], little effort has been devoted to developing CMOEAs in the community of evolutionary computation although both multiobjective EAs (MOEAs) [11] and constraint-handling techniques [12] have been extensively investigated individually.”
Evolutionary computation and structural design: A survey of the state-of-the-art (2005, methodology): “Penalty functions effectively transform a constrained design problem into an unconstrained one by augmenting the objective function with a penalty term whose value determines the amount of constraint violation present in a particular solution [97].”
Artificial bee colony algorithm for large-scale problems and engineering design optimization (2010, background): “For example they require a careful ﬁne tuning of the penalty factors that estimate the degree of penalization to be applied (Smith and Coit 1997; Mezura-Montes and Coello Coello 2005a; Coello Coello 2002).”
Comparison of multi-objective optimization methodologies for engineering applications (2012, background): “A multi-objective global minimum (or maximum) problem is formally defined in Definition 3 [4]:”