Local Fitness Landscape Exploration Based Genetic Algorithms
Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This p...
Main Authors: | Rahul Dubey, Simon Hickinbotham, Mark Price, Andy Tyrrell |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10007811/ |
Similar Items
-
Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability
by: José Santos, et al.
Published: (2017-03-01) -
Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State
by: Itai Tzruia, et al.
Published: (2024-11-01) -
A Fitness Landscape-Based Method for Extreme Point Analysis of Part Surface Morphology
by: Jinshan Sun, et al.
Published: (2025-02-01) -
Cycle Mutation: Evolving Permutations via Cycle Induction
by: Vincent A. Cicirello
Published: (2022-05-01) -
Hitting times of local and global optima in genetic algorithms with very high selection pressure
by: Eremeev Anton V.
Published: (2017-01-01)