Empirical Investigation of the Benefits of Partial Lamarckianism

By Christopher R. Houck, Jeffrey A. Joines, Michael G. Kay, James R. Wilson. 1997

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Citations in the corpus

Population Based Training of Neural Networks (2017, background): “, 2000), or where evolution and learning are both applied to the parameters (Ku & Mak, 1997; Houck et al., 1997; Igel et al., 2005).”
Memetic algorithms and memetic computing optimization: A literature review (2012, methodology): “A rather simple one is to resort to partial Lamarckianism [75] by randomly applying local search with probability pLS < 1.”
Hybrid Genetic Algorithms: A Review (2006, methodology): “Houck et al. [99] found that this form of partial Lamarckian approach outperformed both the pure Lamarckian and the pure Baldwinian approaches on a selected set of test problems.”
A tutorial for competent memetic algorithms: model, taxonomy, and design issues (2005, methodology): “We can also include the approaches discussed in [74] and [75], where partial Lamarckianism or “sniffs” rather than complete local searches are allowed and allocated dynamically during the search.”
Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems (2008, background): “Many researchers [1–3] have shown that EAs perform well for global searching due to their capability of quickly exploring and finding promising regions in the search space, but they take a relatively long time to converge to a local optimum.”
Real-Coded Memetic Algorithms with Crossover Hill-Climbing (2004, background): “Examples are found in Hart (1994), Hart, Rosin, Belew and Morris (2000), Joines and Kay (2002), Houck, Joines, Kay and Wilson (1997) , M ¤ uhlenbein, Schomisch and Born (1991), Renders and Bersini (1994), Renders and Flasse (1996), Rosin, Halliday, Hart and Belew (1997), and Zhang and Shao (2001).”