Keywords
CrossoverForagingGlobal optimizationGenetic algorithmComputer scienceMathematical optimizationMutationMeta-optimizationPID controllerOptimization algorithmOptimization problemAlgorithmMathematicsArtificial intelligenceEngineeringBiologyControl engineering
Affiliated Institutions
Related Publications
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is b...
A modified particle swarm optimizer
Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, w...
Applications of Multi-Objective Evolutionary Algorithms
An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach Us...
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to mul...
Publication Info
- Year
- 2007
- Type
- article
- Volume
- 177
- Issue
- 18
- Pages
- 3918-3937
- Citations
- 471
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
471
OpenAlex
Cite This
Dong Hwa Kim,
Ajith Abraham,
Jae Hoon Cho
(2007).
A hybrid genetic algorithm and bacterial foraging approach for global optimization.
Information Sciences
, 177
(18)
, 3918-3937.
https://doi.org/10.1016/j.ins.2007.04.002
Identifiers
- DOI
- 10.1016/j.ins.2007.04.002