Real-Coded Memetic Algorithms with Crossover Hill-Climbing

By Manuel Lozano, Francisco Herrera, Natalio Krasnogor, Daniel Molina. Evolutionary Computation, 2004

340 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/9a8a3a0f2843f3c45ab8f2a1549ad65949c6cb93

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. Zhihua Cai
4. Wenyin Gong
5. Ferrante Neri
6. Francisco Herrera
7. Sankha Subhra Mullick
8. Pinar Çivicioglu

In Computational Theory and Mathematics

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Zhihua Cai
4. Wenyin Gong
5. Ferrante Neri
6. Sankha Subhra Mullick
7. Pinar Çivicioglu
8. Ville Tirronen

Citations in the corpus

• DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, background): “... producing different kinds of EC for optimization in the continuous domain, such as GAs (Zhong et al. 2004; Herrera et al. 1998; Herrera 2000), evolution strategy (Yao and Liu 1997; Hansen and Kern 2004; Auger and Hansen 2004), evolutionary programming (Yao et al. 1999), particle swarm optimizati”
• A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, methodology): “1) For the CT part, we consider the Parent Centric BLX (PCBLX) operator [59] (an operator that is based on BLXα).”
• Recent advances in differential evolution - An updated survey (2016, methodology): “In other words, while ES, EP, and some other real coded Genetic Algorithms (GAs) require the specification or adaptation of the absolute step size for each variable over iterations, the canonical DE requires only the specification of a single relative scale factor F for all variables.”
• Memetic algorithms and memetic computing optimization: A literature review (2012, methodology): “[116] via the use of negative assortative mating: after picking a solution for recombination, a collection of potential mates is selected and the most diverse one is used.”
• Recent advances in differential evolution: a survey and experimental analysis (2010, background): “In other words, update of the scale factor and thus generation of the offspring is, with a certain probability, controlled in order to guarantee a high quality solution which can take on a key role in subsequent generations, see also Lozano et al. (2004).”
• Hybrid Genetic Algorithms: A Review (2006, background): “Lozano et al. [132] proposed a simple adaptive scheme which sets the probability of local search of each individual to either 1.0 or 0.0625 depending on the individuals fitness compared to the fitness of the current worst individual in the population.”
• Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions (2011, methodology): “[40] RCGA using fuzzy recombination crossover with multiple descendants (RCGA-FR-MDs) Sánchez et al.”
• Accelerating Differential Evolution Using an Adaptive Local Search (2008, background): “According to Lozano et al., real coded MAs (RCMAs) have evolved mainly in two classes depending on the type of LS employed [17].”
• A tutorial for competent memetic algorithms: model, taxonomy, and design issues (2005, methodology): “This is not because MAs are unsuited to these domains—they have been very successfully applied to the fields of multiobjective optimization (see, e.g., [21]–[24], an extensive bibliography can be found in [25]), and numerical optimization (see, e.g., [26]–[32]).”