Genetic algorithms

By Stephanie Forrest. 1996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep learning in neural networks: An overview (2014, neutral): “ estimated (and then exploited) through repeated NN evaluations. RL NNs can also be evolved through Evolutionary Algorithms (EAs) (Rechenberg, 1971; Schwefel, 1974; Holland, 1975; Fogel et al., 1966; Goldberg, 1989) in a series of trials. Here several policies are represented by a population of NNs ”
Reinforcement learning in robotics: A survey (2013, neutral): “, 2005), and general stochastic optimization methods (such as NelderMead (Bagnell and Schneider, 2001), cross entropy (Rubinstein and Kroese, 2004) and population-based methods (Goldberg, 1989)) to approaches coming from optimal control such as differential dynamic programming (DDP) (Atkeson, 1998) ”
Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms (2014, unknown): “In progressive techniques, DM incorporates his preferences in the process in an interactive way (Barbosa and Barreto 2001).”
Firefly Algorithms for Multimodal Optimization (2009, methodology): “ity of pm = 0.05 and a crossover probability of 0.95. For the particle swarm optimization, we have also used the standard version with the learning parameters α ≈ β ≈ 2 without the inertia correction [4, 5, 6]. We have used various population sizes from n = 15 to 200, and found that for most problem”