An Efficient Constraint Handling Method for Genetic Algorithms

By Kalyanmoy Deb. 2000

3,884 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/5bed87f90d116ebbe3222e7b781c72a6045ab6ac

How researchers see this paper

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

In Artificial Intelligence

1. Seyedali Mirjalili
2. Yaochu Jin
3. Carlos Artemio Coello Coello
4. Ponnuthurai Nagaratnam Suganthan
5. Xin-She Yang
6. Amir H. Gandomi
7. Efrén Mezura-Montes
8. Ran Cheng

In Computational Theory and Mathematics

1. Yaochu Jin
2. Seyedali Mirjalili
3. Carlos Artemio Coello Coello
4. Ponnuthurai Nagaratnam Suganthan
5. Xing-yi Zhang
6. Xin-She Yang
7. Amir H. Gandomi
8. Ye Tian

Citations in the corpus

• DEPSO: hybrid particle swarm with differential evolution operator (2003, methodology): “The constraint-handling method is following the criteria [6]: a) any feasible solution is preferred to any infeasible solution; b) among two feasible solutions, the one having better objective function value is preferred; c) among two infeasible solutions, the one having smaller constraint violation”
• Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization (2010, methodology): “(2) and (3) and uses Deb’s feasibility-based rule [6] to determine whether the updated particles replace their corresponding pbest s”
• Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, methodology): “In this experiment, four different constraint handling techniques: superiority of feasible solutions (SF) (Deb 2000), self-adaptive penalty approach (SP) (Tessema andYen 2006), ε -constraint technique (EC) (Takahama and Sakai 2006) and stochastic ranking technique (SR) (Runarsson and Yao 2000), are…”
• Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces (2019, methodology): “1) Constraint-Handling Technique: The feasibility rule [48] serves as the constraint-handling technique in ToP to compare pairwise individuals.”
• MULTI-OBJECTIVE OPTIMIZATION OF FIBER REINFORCED COMPOSITE LAMINATES FOR STRENGTH, STIFFNESS AND MINIMAL MASS (2006, background): “Equality constraints, although not explicitly stated, can be handled by converting them to inequality constraints [27].”
• An enhanced genetic algorithm for structural topology optimization (2006, methodology): “If there are no feasible individuals existing in the population, which is often seen in the early generations of the GA, a pre-defined worst value which can guarantee that any infeasible solution is worse than any feasible one, rather than 0 as recommended in Deb’s method [55], is assigned to f ∗ in”
• Push and Pull Search for Solving Constrained Multi-objective Optimization Problems (2017, methodology): “The ensemble of constraint-handling methods (ECHM) [19] uses three different constraint-handling techniques, including epsilon constrainthandling (EC) [11], self-adaptive penalty functions (SP) [9] and superiority of feasible solutions (SF) [20].”
• Multiobjective Multifactorial Optimization in Evolutionary Multitasking (2017, background): “ing distance (CD) in constrained multiobjective optimization are directly adopted from [11] and [31].”
• Reference point based multi-objective optimization using evolutionary algorithms (2006, background): “One of the two objectives is to minimize the cost of fabrication and other is to minimize the end deflection of the welded beam [19]:”