Adapting Crossover in Evolutionary Algorithms
By W. Spears. 1995
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- Yoshua Bengio
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Citations in the corpus
- Population Based Training of Neural Networks (2017, background): “PBT is perhaps most analogous to evolutionary strategies that employ self-adaptive hyperparameter tuning to modify how the genetic algorithm itself operates (Bäck, 1998; Spears, 1995; Gloger, 2004; Clune et al., 2008) – in these works, the evolutionary hyperparameters were mutated at a slower rate t”
- Exploring dynamic self-adaptive populations in differential evolution (2006, methodology): “Since self-adaptation has been shown to be a very effective method for dynamically adjusting evolutionary parameters [9, 18 ,21], we thus propose a similar treatment for dynamically adjusting the population size.”
- Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, unknown): “Spears [186] suggested a simple self-adaptive”
- Real-Coded Memetic Algorithms with Crossover Hill-Climbing (2004, background): “4 Study of the Adaptive pLS Mechanism There are, at least, two ways to study the operation of an adaptive mechanism for GAs (Spears, 1995).”
- Recent advances in differential evolution - An updated survey (2016, background): “Starting with the landmark paper on Self adaptive DE (SaDE) [150,151], there has been a growing trend of selecting the offspring generation strategies (a combination of a mutation and a crossover strategy) from a pool [172] as well as adapting the control parameters F and Cr based on success histori”
- Parameter Control in Evolutionary Algorithms (2007, background): “Spears [80], following [31], experimented with individuals containing an extra bit to determine whether one-point crossover or uniform crossover is to be used (bit 1/0 standing for one-point/uniform crossover, respectively).”
- Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems (2006, unknown): “subject to evolution (self-adaptive parameter control) [13].”