Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

By Kalyanmoy Deb, Hans-Georg Beyer. Evolutionary Computation, 2001

448 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/286cae199cd76aa13cfad5e3545184c064bf1a8d

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Citations in the corpus

A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization (2002, background): “Using variable-wise recombination operators, a past study (Deb and Beyer, 2000) has clearly shown the advantage of using a parent-centric operator (SBX) over a number of other recombination operators .”
A taxonomy for the crossover operator for real‐coded genetic algorithms: An experimental study (2003, background): “If the crossover operator also has a tendency to reduce the diversity of the population, the overall search algorithm may not have adequate power to adapt to any function landscape.(54) This guideline reveals the importance of considering the exploration and relaxed exploitation intervals for design”
Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection (2008, methodology): “We use the simulated binary crossover (SBX) operator and polynomial mutation (Deb and Beyer, 2001) for NNIA, PESA-II, NSGA-II and SPEA2.”
MOEA/D with Adaptive Weight Adjustment (2014, methodology): “…rand from (0 , 1) , set 2 Reproduce: Set r 1 = i and randomly choose two indices r 2 and r 3 from P , construct a solution ¯ y from x r 1 , x r 2 and x r 3 by the SBX operator and apply the polynomial mutation operator (Deb and Beyer, 2001) on ¯ y with probability p m to generate a new solution y .”
Parameter Control in Evolutionary Algorithms (2007, background): “And whereas the step-size actually self-adapts to quasi-optimal values [9, 21], the covariance matrix that is learned by the correlated SA-ES is not the actual inverse of the Hessian [4].”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “Both BLX and SBX were analyzed in detail by Deb and Beyer in [8] and [24].”
Real-Coded Memetic Algorithms with Crossover Hill-Climbing (2004, background): “In fact, in the recent past, RCGAs with some of these crossovers have been demonstrated to exhibit self-adaptive behaviour similar to that observed in evolution strategies and evolutionary programming approaches (Deb and Beyer, 2001; Kita, 2001).”
ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems (2006, background): “• Crossover: Simulated binary crossover [18] with proba-”
Adaptive Particle Swarm Optimization (2008, methodology): “To compare the diversity in the search by the APSO and the traditional PSO, a yardstick proposed in [56] is used here, called the “population standard deviation,” which is denoted by psd as psd = √√√√ ⎡ ⎣ N ∑”