Meta-Lamarckian learning in memetic algorithms

By Yew-Soon Ong, Andy J. Keane. IEEE Transactions on Evolutionary Computation, 2004

678 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/9c2bf6d2602e03268e9e7113c5461355eaae73ed

How researchers see this paper

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

In Artificial Intelligence

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Pinar Çivicioglu
4. Seyedali Mirjalili
5. Ferrante Neri
6. Ajith Abraham
7. Seyed Mohammad Mirjalili
8. Carlos Artemio Coello Coello

In Computational Theory and Mathematics

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Pinar Çivicioglu
4. Ferrante Neri
5. Seyedali Mirjalili
6. Ajith Abraham
7. Seyed Mohammad Mirjalili
8. Sankha Subhra Mullick

Citations in the corpus

• A Multi-Facet Survey on Memetic Computation (2011, methodology): “[29], continuous optimization [30], [31], dynamic optimization”
• Recent advances in differential evolution - An updated survey (2016, methodology): “For assignment of credit the relative fitness improvement measure proposed in [138] was used.”
• Ensemble of Constraint Handling Techniques (2010, methodology): “As ECHT employs different constraint handling methods each having its own population, it can be compared with hybrid methods like memetic algorithms [32]–[34].”
• The exploration/exploitation tradeoff in dynamic cellular genetic algorithms (2005, methodology): “A different alternative is using memetic algorithms [21], [ 22 ], in which local search is combined with the genetic operators in order to promote local exploitation.”
• Multifactorial Evolution: Toward Evolutionary Multitasking (2016, neutral): “the principle of Lamarckism [31], [32].”
• Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “If the SPX succeeds in improving upon the starting solution, a replacement occurs according to a meta-Lamarckian logic Ong and Keane (2004).”
• Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, methodology): “In this paper, MA-S2 [59] stands for an adaptive Meta-Lamarckian learning-based MA that employs a stochastic approach (the biased roulette wheel strategy) making use of the knowledge gained online to select a suitable local method with the GA.”
• Bio-inspired computation: Where we stand and what's next (2019, methodology): “local search method [315], or to choose the mutation operator [312].”
• Memetic algorithms and memetic computing optimization: A literature review (2012, methodology): “Meta-Lamarckian learning is an extension and an evolution of the hyperheuristic MAs and especially the choice functions and constitutes a fairly general and flexible framework for algorithmic design, see [164].”
• An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling (2007, methodology): “2) Adaptive Meta-Lamarckian Learning Strategy: Inspired by the idea of Ong and Keane’s work [ 28 ], we not only apply multiple neighborhoods but also adopt the effective adaptive meta-Lamarckian learning strategy to decide which neighborhood to be chosen for local search to reward the utilization ti”
• Accelerating Differential Evolution Using an Adaptive Local Search (2008, background): “Ong and Keane proposed meta-Lamarckian learning in MAs that adaptively chooses among multiple memes during a MA search [19].”