Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence

By Kalyanmoy Deb, Tushar Goel. International Conference on Evolutionary Multi-Criterion Optimization, 2001

490 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/c4e6a4891a41f332a8e83079b33369c176a76344

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Citations in the corpus

Response surface approximation of pareto optimal front in multi-objective optimization (2004, background): “Some of the latest ones include strength Pareto evolutionary algorithm (Zitzler and Thiele) [6], Pareto archived evolutionary strategies (PAES by Knowles and Corne) [7], elitist nondominated sorting genetic algorithm (NSGA-II by Deb et al.) [8], and controlled elitist non-dominated sorting genetic a”
Multiobjective Multifactorial Optimization in Evolutionary Multitasking (2017, background): “Similar variants have also been used by Deb and Goel [47] while verifying the effects of controlled elitism in the NSGA-II.”
The balance between proximity and diversity in multiobjective evolutionary algorithms (2003, methodology): “Simultaneously to the discovery of new MOEAs, research is also being devoted to investigating which components are the most important in designing competent MOEAs along with guidelines on the influence of certain operators on the performance of MOEAs [ 12 ], [7], [13], [14].”
Multi-objective optimization of gear unit design to improve efficiency and transmission error (2022, background): “Therefore, new evolutionary multiple objective algorithms using several mechanisms such as the concept of dominance, niching, elitism, etc. have been implemented ([59], [60]).”
MULTI-OBJECTIVE OPTIMIZATION OF FIBER REINFORCED COMPOSITE LAMINATES FOR STRENGTH, STIFFNESS AND MINIMAL MASS (2006, methodology): “This method, has been shown to provide improved convergence to the true Pareto-optimal front when compared to a standard elitist NSGA-II [28].”
Exergetic Optimization of Shell-and-Tube Heat Exchangers Using NSGA-II (2012, methodology): “Following the definition by Deb and Goel [27], an individual X (a) is said to constrain-dominate an individualX (b) if any of the following conditions are true: 1.”
Thermo‐economic‐environmental multiobjective optimization of a gas turbine power plant with preheater using evolutionary algorithm (2011, methodology): “The crowding distance metric proposed by Deb and co-workers [42,43] is utilized, where the crowding distance of an individual is the perimeter of the rectangle with its nearest neighbors at diagonally opposite corners.”