Handbook Of Genetic Algorithms

By L. Davis. 1990

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Citations in the corpus

SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping (2020, methodology): “The coarse-grained allocation with five 2MB objects corresponds to CLS = [ 0 , 0 ] , indicating that both 1MB and 2MB sizes are mapped to the same size-class with id 1 and object size is 2MB. CNT = [ 5 ] contains the number of objects assigned to each size class from id = 0 ... Max ( CLS ) .”
Genetic algorithm in search and optimization: the technique and applications (1998, background): “We change the above test function to the following function which introduces four optimal solutions of which only one is the global optimal solution (still at (3,2)): Minimize f2(x1; x2) = (x21 + x2 11)2 + (x1 + x22 7)2 + 0:1[(x1 3)2 + (x2 2)2] 6 x1; x2 6: (8) A contour plot of the above function is”
Search‐based software test data generation: a survey (2004, background): “Davis [17] supports this view.”
Decision support for irrigation project planning using a genetic algorithm (2000, background): “Although John Holland and his colleagues pioneered the concept of a `genetic algorithm' at the University of Michigan in the early 1970s (Davis, 1991), it has recently been applied to `real-world' problems.”
A tutorial for competent memetic algorithms: model, taxonomy, and design issues (2005, background): “It is now well established that pure EAs are not well suited to fine tuning search in complex combinatorial spaces and that hybridization with other techniques can greatly improve the efficiency of search [3]–[6].”
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (2001, methodology): “It is well-known {and supported both by theoretical (Wolpert and Macready, 1997) and empirical ( Davis, 1991 ) results{ that the appropriate utilization of problem-dependent knowledge within the EA redounds to highly efiective algorithms.”
Hybrid Genetic Algorithms for Feature Selection (2004, background): “Three ways of hybridizing are suggested in [4]: problem-specific encoding, the use of special genetic operators, and the incorporation of the good features of classical algorithms.”
Model identification of ARIMA family using genetic algorithms (2005, background): “Although many studies have presented applications of GA for time series [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35], these applications generally have focused on the problem of parameter estimation.”
A new optimization method: Big Bang-Big Crunch (2006, background): “Genetic algorithms (GA) [1–4] are a class of stochastic and global optimization which model the basic principles and mechanisms of Mendelian genetics and evolutionary theory along with population genetics to tackle hard search and optimization problems.”
A New Method for Generating Pore-Network Models of Porous Media (2010, methodology): “Genetic algorithms have been used to solve difficult problems with objective functions that are not well behaved, i.e., they do not possess properties such as continuity, differentiability, etc. (Davis 1991; Goldberg 1989; Holland 1975; Michalewicz 1994; Houck et al. 1995).”