Search biases in constrained evolutionary optimization

By Thomas Philip Runarsson, Xin Yao. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2005

481 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/55295fa6358a423929fed8b051297c55aee5171c

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. Yong Wang
2. Ponnuthurai Nagaratnam Suganthan
3. Efrén Mezura-Montes
4. Seyedali Mirjalili
5. Carlos Artemio Coello Coello
6. Zixing Cai
7. R. Venkata Rao
8. Vimal J. Savsani

In Computational Theory and Mathematics

1. Yong Wang
2. Ponnuthurai Nagaratnam Suganthan
3. Efrén Mezura-Montes
4. Zixing Cai
5. Carlos Artemio Coello Coello
6. Seyedali Mirjalili
7. R. Venkata Rao
8. Vimal J. Savsani

Citations in the corpus

• A ranking selection-based particle swarm optimizer for engineering design optimization problems (2008, methodology): “... Yao 2000), a GA with a feasibility-based rule (Deb 2000), inverted-shrinkable Pareto archived evolution strategy method (Aguirre et al. 2004), a PSO incorporated with a turbulence operator and a decision-making scheme (Pulido and Coello 2004), a simple multimembered evolution strategy (Mezura-Mo”
• Ensemble of Constraint Handling Techniques (2010, methodology): “Table VIII compares the results obtained by various stateof-the art algorithms using ES, such as (ES + SR), SMES, ATMES, multiobjective [4], and improved stochastic ranking (ISR) [21].”
• Differential evolution with dynamic stochastic selection for constrained optimization (2008, methodology): “Thirteen benchmark functions taken from [ 4 ] are used to test this straightforward method, in which the value of M is set to 5 as recommended in [9].”
• Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “SR has been further developed by Runarsson and Yao [127] in one of the earliest approaches focused on using fitness approximation for constrained numerical optimization.”
• Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems (2009, background): “…which have helped researchers formulate such new stochastic algorithms as the genetic algorithm (Chootinan & Chen, 2006), evolutionary optimization (Runarsson & Yao, 2005), and particle swarm optimization (Dong, Tang, Xu, & Wang, 2005), are of little practical use in solving constraint…”
• An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions (2017, methodology): “The penalty function method is widely used due to its simplicity in the constraint handling (Runarsson and Yao (2005)).”
• Constraint Handling in Multiobjective Evolutionary Optimization (2009, methodology): “In [19] and [20], Runarsson and Yao introduce the stochastic ranking method to achieve a balance between objective and penalty functions stochastically.”
• A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization (2006, methodology): “The other approaches in the comparison include: 1) Self-Adaptive Fitness Formulation (SAFF) method [9]; 2) Simple Multimembered Evolution Strategy (SMES) method [16]; 3) Stochastic Ranking (RY) method [17]; 4) Inverted-Shrinkable PAES (IS-PAES) method [26]; 5) Derivative-Free Filter Simulated Anneal”
• Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, background): “2007 ), improved stochastic ranking (ISR) (Runarsson and Yao 2005 ) and ensemble of constraint handling technique strategy (ECHT-EP) (Mallipeddi and Suganthan 2010 ).”