On benchmarking functions for genetic algorithms

By Jason G. Digalakis, K. Margaritis. International Journal of Computational Mathematics, 2001

410 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/a8fe54b39bd06fd703520bbd134c08a87e0baff0

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

In Computational Theory and Mathematics

Citations in the corpus

The Ant Lion Optimizer (2015, neutral): “The test functions are divided the three groups: uni-modal (Table 1), multi-modal (Table 2), and composite functions (Table 3) [20,56–58].”
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, background): “As shown in Appendix 1, the test functions are divided the three groups: unimodal, multi-modal, and composite functions [48–51].”
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, methodology): “We also follow the same process and employ 19 benchmark functions in the literature as test beds for comparison [7, 49-51].”
Grasshopper Optimisation Algorithm: Theory and application (2017, background): “The test functions are unimodal, multimodal, and composite [40-43].”
Chemical-Reaction-Inspired Metaheuristic for Optimization (2010, background): “The adjustments rely on the experience and preferences of researchers [36].”
Emperor penguin optimizer: A bio-inspired algorithm for engineering problems (2018, background): “Unimodal benchmark test functions A1.”
A novel algorithm for global optimization: Rat Swarm Optimizer (2020, methodology): “The proposed algorithm is evaluated on thirty eight benchmark test functions which are divided into four main categories: Unimodal (Digalakis and Margaritis 2001), Multimodal (Yang 2010a), Fixed-dimension Multimodal (Digalakis and Margaritis 2001; Yang 2010a), and CEC-15 special session functions (C”
Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning (2024, neutral): “In this article, we have used different standard composition model (CM), multimodal (MM), and unimodal (UM) [62, 63] benchmarks to accurately evaluate the Puma algorithm, each of the benchmarks has a different difﬁ-culty.”
Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications (2017, background): “These functions are divided into four main categories such as unimodal [11] , multimodal [62] , fixed-dimension multimodal [11,62] , and composite functions [37] .”