Evolutionary Optimization Algorithms

By L. Fortuna, G. Rizzotto, M. Lavorgna, G. Nunnari, M. Xibilia, R. Caponetto. 2001

477 citations recorded by the source corpus.

Subfields: Artificial Intelligence

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. Florian Hase
2. Alán Aspuru-Guzik
3. Yuan Yuan
4. Qi Wang
5. Yajing Zhang
6. Tinghui Liu
7. Tong Yu
8. Hong Zhu

Citations in the corpus

• Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, methodology): “, 2012), such as evolutionary algorithms (Simon, 2013; Orive et al., 2014), particle swarm optimization (Eberhart and Shi, 1998; Lorenzo et al.”
• Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking (2016, methodology): “Fortunately, inspired by the success of natural computation [41], the clone selection algorithm [42], [43] is utilized to solve this problem.”
• A comprehensive survey on NSGA-II for multi-objective optimization and applications (2023, methodology): “There are a limited number of publications about the theoretical research of MOEAs including NSGAII to help investigate the operator impact on optimization problems (Simon 2013; Ma et al. 2018), which leaves a large gap for the further generalizations, improvements and applications of NSGA-II.”
• PyGAD: an intuitive genetic algorithm Python library (2021, background): “These naturally-inspired algorithms are called evolutionary algorithms (EAs) [1] where the initial solution (or individual) to a given problem is evolved across multiple iterations aiming to increase its quality.”
• Phoenics: A Bayesian Optimizer for Chemistry (2018, methodology): “− 3 More sophisticated methods include genetic algorithms 20,21 based on evolution strategies, 22,23 which are, for example, applied to the optimization of nanoalloy clusters, 24 or to resolve electronic spectra of rotamers of organic compounds.”