Adaptive probabilities of crossover and mutation in genetic algorithms

By M. Srinivas, Lalit M. Patnaik. 1994

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Citations in the corpus

FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function (2020, methodology): “The second stage of the proposed method is an iterative process based on adaptive genetic algorithms [32].”
Optimization of 3d trusses with adaptive approach in genetic algorithms (2006, methodology): “Srinivas and Patnaik [17] disrupted solutions with a constant probability of mutation.”
Choosing Mutation and Crossover Ratios for Genetic Algorithms - A Review with a New Dynamic Approach (2019, unknown): “Figure 2a depicts a crossover process [37].”
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms (2007, background): “They differ from random sampling algorithms, as they can direct the search towards relatively prospective regions in the search space [9].”
Degree of population diversity - a perspective on premature convergence in genetic algorithms and its Markov chain analysis (1997, methodology): “[8] M. Srinivas and L. M. Patnaik, “Adaptive probabilities of crossover and mutation in genetic algorithms,”IEEE Trans.”
Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, background): “3) Controlling mutation and crossover rates: An adaptive approach to controlling both mutation and crossover rates was presented by Srinivas and Patnaik [204].”
Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis (2021, methodology): “Crossover is used to generate new chromosomes by swapping proportions of the genes of different chromosomes, which represent the solution, in order to obtain the mixing of solutions in the search space [34].”
Bio-inspired computation: Where we stand and what's next (2019, background): “For long time ago it has been proposed to increase the diversity between parents and offspring when the population converges to compensate for the lack of diversity [310].”
Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models (2011, unknown): “05 [51], and 0.”
Truss optimization on shape and sizing with frequency constraints based on parallel genetic algorithm (2011, methodology): “One of best effective niche techniques, clearing method (P’etrowski 1996), and adaptive parameter adjustment ( Srinivas and Patnaik 1994 ) are used to maintain population diversity.”