Simulated Binary Crossover for Continuous Search Space

By Kalyanmoy Deb, R. Agrawal. 1995

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Citations in the corpus

Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems (2020, methodology): “This concept will be used to reengineer the NLDT*, a discussion regarding which is provided in the next section.”
Multi-objective optimization using evolutionary algorithms (2001, background): “A probability distribution which depends on the difference between the two parent variable values is often used to create two new numbers as child values around the two parent values [5].”
A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “polynomial mutation [65] are used in all the considered algorithms except dMOPSO.”
A review on genetic algorithm: past, present, and future (2020, background): “Deb and Agrawal [34] developed a real-coded crossover operator, which is based on characteristics of single-point crossover in BGA.”
Optimization of natural frequencies of bidirectional functionally graded beams (2006, methodology): “The process of crossover is performed with the simulated binary crossover (SBX) operator ( Deb and Agrawal 1995 ).”
Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, methodology): “Then the genetic operators, i.e., simulated binary crossover (SBX) and polynomial mutation [51], are executed on them to generate a new solu-tion y , and ﬁnally y is used to update the ideal point and the current population.”
Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization (2008, methodology): “We use the standard variable-wise SBX crossover [20] on 50% of variables, on an average.”
Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design (2005, background): “It should be noted that all of the tested algorithms used simulated binary crossover (SBX) [48], polynomial mutation [13], and elitism [13].”
Evaluating the -Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions (2005, methodology): “Since all test problems involve real-valued decision variables, we have used the SBX recombination operator (Deb and Agrawal, 1995) and the polynomial mutation operator (Deb and Goyal, 1996) to create an offspring solution.”
A fast and elitist multiobjective genetic algorithm: NSGA-II (2002, methodology): “We use the single-point crossover and bitwise mutation for binary-coded GAs and the simulated binary crossover (SBX) operator and polynomial mutation [6] for real-coded GAs.”
Evolving Deep Convolutional Neural Networks for Image Classification (2017, methodology): “The distribution index of SBX and Polynomial mutation are both set to be 20, and their associated probabilities are specified as 0.9 and 0.1, respectively.”
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “In the proposed RVEA, the widely used genetic operators, i.e., the simulated binary crossover (SBX) [66] and the polynomial mutation [67] are employed to create the offspring population, as in many other MOEAs [28], HypE, GrEA, and Authorized licensed use limited to: Central South University.”
Indicator-Based Selection in Multiobjective Search (2004, background): “Although for the variation parameter settings described above, the choice for the parameter κ does not influence 3 For the continuous problems, the individuals are coded as real vectors, where the SBX-20 operator is used for recombination and a polynomial distribution for mutation [4].”
Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization (2017, methodology): “Once the mating parents are chosen, we use the popular simulated binary crossover [36] and the polynomial mutation [37] for offspring reproduction.”
A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I (2014, methodology): “For real-valued chromosomes in [63], simulated binary crossover (SBX) [70] and standard polynomial mutation operators are employed.”
Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, methodology): “3) TiGE-2, NSGA-II-CDP, C-TAEA, and CMOEA-MS use simulated binary crossover [43] and polynomial mutation [44] to generate offspring solutions, while PPS and ToP use differential evolution [45] and polynomial mutation.”
A new crossover operator for real coded genetic algorithms (2007, methodology): “Deb and Agrawal [19] developed the Simulated Binary Crossover (SBX), which simulates the working principle of the single-point crossover operator on binary strings in continuous domain.”
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence (2001, background): “ZDT6 :8>>><>>>:Minimize f1(x) = 1 exp( 4x1) sin6(6 x1) Minimize f2(x) = g(x) h1 (x1=g(x))2i g(x) = 1 + 9 h(P10 i=2 xi)=9i0:25 x1 2 [0; 1℄; xi 2 [ 5; 5℄; i = 2; : : : ; 10: (6) The above problem should not ause mu h diÆ ulty in onverging to the orre t front (g(x) = 1), but auses diÆ ulty in maintaini”