Ensemble of Constraint Handling Techniques

By Rammohan Mallipeddi, Ponnuthurai Nagaratnam Suganthan. IEEE Transactions on Evolutionary Computation, 2010

438 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/04711c803512c71c765bca39c964b9037fe075e8

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

In Computational Theory and Mathematics

Citations in the corpus

Recent advances in differential evolution - An updated survey (2016, methodology): “An updated survey Swagatam Das a,n, Sankha Subhra Mullick a, P.N. Suganthan b,n a Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700108, India b School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore a r t i c l e i n f o Art”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “outperformed by the ECHT that uses DE instead [188].”
Problem Definitions and Evaluation Criteria for the CEC 2010 Competition on Constrained Real- Parameter Optimization (2010, background): “In [5] the authors generated some scalable constrained problems.”
Differential evolution with multi-population based ensemble of mutation strategies (2016, background): “Inspired by the success, the idea ensemble learning has been further explored to solve constrained optimization problems [32].”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “Integrating DEGL in ensemble of DE schemes [S210], [52], [80], [100] should also be studied in the future.”
Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation (2010, methodology): “In this paper, eighteen benchmark problems for the CEC2010 competition and special session on “Single Objective Constrained Real-Parameter Optimization” [29] are solved.”
Bio-inspired computation: Where we stand and what's next (2019, background): “such as neighborhood sizes [247], constraint handling techniques [248] or niching [249] among others.”
Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, methodology): “…2005), adaptive tradeoff model evolutionary strategy (ATMES) (Wang et al. 2008), multi-objective evolutionary strategy (Wang et al. 2007), improved stochastic ranking (ISR) (Runarsson andYao 2005) and ensemble of constraint handling technique strategy (ECHT-EP) (Mallipeddi and Suganthan 2010).”
An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems (2012, background): “The results of DE and EP are taken from the previous work of Mallipeddi and Suganthan (2010).”