The document discusses genetic algorithms and their applications in various research areas including image processing, data science, and cloud computing. It outlines different techniques, operators, and variations of genetic algorithms such as adaptive GA and multi-objective GA, as well as other machine learning methods like deep learning and optimization algorithms. Additionally, it provides contact information for further inquiries.

1. GE N E T I C A L GO R I T H M
P R O J E C T S
www.matlabsimulation.com/genetic-algorithm-projects/

2. Genetic Algorithm – Research Zones
These are the areas which uses genetic algorithm research projects,
Image Processing
Networking
Internet of
Things
Data Science
Cloud Computing

3. Techniques for Genetic Algorithm
This kind of algorithm uses the genetic projects which are listed below,
Termination // After
maximum iteration
Selection // Fitness
Evaluation
Crossover and
Mutation// Genetic
Operators
Return Results ()
Initialization of
Candidate Solutions //
As Chromosomes

4. Unified Procedures: Genetic Algorithm Projects
Deep
Learning
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Heuristics
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Machine
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• Deep Q-Learning and also SARSA
• Convolution Neural Network
• Spiking and also Quantum Nets
• Gated Recurrent Neural Network
• Black Widow Optimization
• Killer Whale Algorithm
• Spotted Hyena Optimizer
• Particle Swarm Optimization
• Decision Forest and also CART
• Support Vector Machines
• Neuro-Fuzzy Model
• Naïve Bayes Classifier
The following outline of your genetic algorithm projects are listed below,

5. Enriched Genetic Algorithm Versions
Let’s we see the algorithms versions which are covered by genetic Projects,
GA through Inversion Operator
Adaptive GA (AGA)
Multi-Objective GA (MO-GA)
Parallel Genetic Algorithm
Non-Dominated Sorting GA
(NSGA-II)

6. Contact Us
24
www.matlabsimulation.com
+91 9444856435
matlabguide@gmail.com