The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination

By L. Eshelman. 1990

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Citations in the corpus

Local Rule-Based Explanations of Black Box Decision Systems (2018, background): “arning. We aim instead at generating new instances that separate well the decision boundary of the black box b. The usage of classifiers within fitness functions of genetic algorithms can be found in [5, 8, 12, 41]. However, the classifier they use is always the one for which the population must be ”
Evolving effective micro behaviors in RTS game (2014, methodology): “We used CHC elitist selection in which offspring compete with their parents as well as each other for population slots in the next generation [26], [27].”
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms (2011, background): “• CHC : The key idea of the CHC algorithm [9] concerns the combination of a selection strategy with a very high selective pressure and several components inducing a strong diversity.”
Feature selection for support vector machines by means of genetic algorithm (2003, methodology): “The GA used here is the CHC algorithm by Eshelman [ 5 ], which was reported to perform a more aggressive and faster search strategy than the traditional Simple GA [7].”
Automated phylogenetic detection of recombination using a genetic algorithm. (2006, methodology): “Consequently, we utilize an aggressive population based hill-climber - the CHC genetic algorithm (Eshelman, 1991; Kosakovsky Pond and Frost, 2005a) - to search the space of candidate models.”
Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization (2008, methodology): “The ranking procedure with a small will assign each population member into a different class and the selection of N members from the combined population of size 2N degenerates to choosing the best N solutions of the combined parent–offspring population, a procedure followed in both CHC and (l + k)-e”
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power (2010, background): “PDFC [8] C = 100.0, d = 0.25, tolerance = 0.001, epsilon = 1.0EÀ12, PDRFtype = Gaussian NNEP [35] Hidden nodes = 4, Transfer = Product unit, Generations = 200 IS-CHC+1-NN [6] [20] [16] Population size = 50, Number of evaluations = 10,000, a equilibrate factor = 0.5 Percent of change in restart = 0.3”
A survey on feature selection methods (2014, methodology): “Table 3 gives the results of running the CHCGA algorithm with RBF as the wrapper.”
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, methodology): “1) CHC Genetic Model: The approach that is proposed in [35] considers the use of a speciﬁc GA, the CHC algorithm [56].”
How to Solve It: Modern Heuristics (2000, background): “, MPX operator [329]) to locally optimal individuals [56, 195, 329, 302, 471, 129], i.”
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem (2000, methodology): “A unique feature of our memetic algorithm is the restart mechanism borrowed from Eshelman’s CHC algorithm [73].”
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study (2003, background): “3) CHC Adaptive Search Algorithm: During each generation the CHC [10] develops the following steps.”
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, unknown): “Finally, the considered EA is the good performing real-coded CHC [65].”
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling (2013, methodology): “EUS makes use of the well-known CHC algorithm [15], holding a good balance between exploration and exploitation, to evolve the initially random population.”
Evolutionary computation and structural design: A survey of the state-of-the-art (2005, background): “There are also many hybrid models incorporating various features of the above paradigms, including the CHC algorithm [15], the structured GA [16], the breeder GA [17], the messy GA [18], and many others.”
Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy (2009, methodology): “As the evolutionary computation method in the core of EPS, we have used the CHC model (Eshelman, 1991; Cano et al., 2003) and the intelligent genetic algorithm (IGA; Ho et al., 2002).”