The compact genetic algorithm

By G. Harik, Fernando Graça Lobo, David E. Goldberg. 1998

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Citations in the corpus

CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models (2019, methodology): “Future work involves speeding up the genetic algorithm by techniques like [Harik et al., 1999] and [Mitchell et al., 1994] A comparison between the introduced fairness metric and previous metrics would also be useful.”
A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables (2016, background): “distribution algorithms (EDAs) [28], compact genetic algorithms [29], Bayesian optimization algorithms (BOAs) [30], and dependency structure matrix (DSM)-driven genetic algo-”
A Survey of Optimization by Building and Using Probabilistic Models (2000, methodology): “For instance, the simple GA with uniform crossover which randomly picks a value on each position from either of the two parents works asymptotically the same as the socalled univariate marginal distribution algorithm [29] that assumes that the variables are independent [16, 25, 36].”
A survey on optimization metaheuristics (2013, methodology): “Common univariate EDAs include Population Based Incrementa l Learning (PBIL)[14], Univariate Marginal Distribut ion Algorithm (UMDA) [188] and Compact Genetic Algorithm (cGA) [125].”
Elitism-based compact genetic algorithms (2003, methodology): “Although Harik et al. [ 9 ] investigated a relation between tournament size and selection pressure (in the cGA) by employing a global schema theorem, the relation was verified only in the context of a specific problem involving concatenation of ten copies of a three-bit, fully deceptive function wit”
Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, unknown): “6 A further extension is suggested with the BIPOP-CMA-ES by Hansen [96], [97].”
An introduction and survey of estimation of distribution algorithms (2011, methodology): “The compact genetic algorithm (cGA) [26] is another incremental univariate EDA.”
Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms (2006, methodology): “Objective function C is cited from [19].”
A machine learning based credit card fraud detection using the GA algorithm for feature selection (2022, methodology): “This flowchart represents the compact version of the implementation of the pseudo code in Algorithm 1 and Algorithm 2 [30].”
Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization (2014, methodology): “Popular model building algorithms include cGA [43], BOA [44], and hBOA [45].”
A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice (2005, background): “[18], the population is replaced by a single probability vector like in PBIL.”
Genetic algorithms for modelling and optimisation (2005, background): “In tandem with this, more sophisticated approaches to directing the evolution of a GA population are aimed at improving performance on classes of problem known to be difficult for GAs [25,16,26].”
Linkage Problem, Distribution Estimation, and Bayesian Networks (2000, methodology): “In cGA, the order-one behavior of the simple genetic algorithm with uniform crossover is approximated (Harik et al., 1998).”
BOA: the Bayesian optimization algorithm (1999, methodology): “This is the basic principle of the population based incremental learning (PBIL) algorithm (Baluja, 1994), the compact genetic algorithm (cGA) (Harik et al., 1997), and the univariate marginal distribution algorithm (UMDA) (Mühlenbein & Paaß, 1996).”