Genetic algorithms and Machine Learning

By David E. Goldberg, John H. Holland. 1988

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “A central idea in evolutionary computation (Goldberg and Holland, 1988) is that evolutionary processes, similar to those that occurred in nature during the course of billions of years (Darwin, 1859; Dobzhansky, 1970), can be simulated with computer software.”
SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping (2020, methodology): “Genetic algorithm (GA) aims to evolve and improve an entire population of individuals via the nature-inspired mechanisms such as crossover, mutation, and selection [5, 8, 18].”
Hardware implementation of memristor-based artificial neural networks (2024, unknown): “Gradient-free methods such as the Particle Swarm optimization 168 , Genetic Algorithms 169 and Simulated Annealing 170 algorithms are more demanding from a computational point of view, andhence,theyarerarelyemployedforANNtraining,bywhichtheylie beyond the scope of this article.”
Clustering algorithms: A comparative approach (2016, background): “, simulated annealing [38] or genetic algorithms [39]) to find the parameter configuration providing the best performance of a given algorithm.”
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications (2020, methodology): “Evolutionary optimization is another class of generic population-based meta heuristic DFO algorithms, and includes particle swarm optimization methods [12] and genetic algorithms [13].”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “Such agents evolve though a genetic algorithm (Goldberg and Holland, 1988) that allocates larger “fitness” values to agents that perform better a series of computational tasks (each one consisting in assigning a given output to 45 At β = 0 (infinite temperature) all configurations are equiprobable,…”
Improving the shuffled complex evolution scheme for optimization of complex nonlinear hydrological systems: Application to the calibration of the Sacramento soil‐moisture accounting model (2010, methodology): “…of particles (samples) in the parameter space through the use of competitive evolution schemes, such as the simplex scheme [Nelder and Mead, 1965], swarm intelligence [Eberhart and Kennedy, 1995], and genetic mutation/selection [Goldberg and Holland, 1988], to reproduce better offspring particles.”
The Deep Learning Compiler: A Comprehensive Survey (2020, methodology): “an only perform auto-tuning on NVIDIA GPUs, while TVM can apply auto-tuning to CPUs (x86 and ARM), mobile GPUs and NVIDIA GPUs [30]. TVM and TC use different tuning approaches: TC uses genetic search [41] and TVM uses two machine learning models (i.e., GBT and TreeGRU). PlaidML can only apply auto-t”
Coronavirus herd immunity optimizer (CHIO) (2020, methodology): “Evaluation-based Genetic algorithm (GA) [9], evolution strategy (ES) [16], genetic programming (GP) [17], and biogeography-based optimizer (BBO) [18] Swarm-based Particle swarm optimization (PSO) [10], ant colony optimization (ACO) [11], cuckoo search (CS) [19], bat algorithm (BA) [20], ant lion opt”
Multicriteria Optimization and Decision Making: Principles, Algorithms and Case Studies (2024, unknown): “Theoretically, it has been shown that for decomposable functions recombination can enhance the frequency of parts of the individuals that contribute to a good fitness [37] and decrease deteriorating effects of mutation [10].”
Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications (2022, methodology): “In GA, population members are updated based on three main operators: selection, crossover, and mutation [19].”