Genetic algorithms - advanced techniques
Advanced workshop training on the design and implementation of genetic algorithms in complex optimization and machine learning problems. The program combines the theoretical foundations of computational evolution with the practical application of genetic algorithms in real-world scenarios. Participants, through an intensive workshop, learn genetic operator design techniques, parameter adaptation strategies, and hybridization methods with other computational techniques. The training emphasizes the practical aspects of implementing and optimizing genetic algorithms in production projects.
Issues
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Theory of genetic algorithms
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Design of genetic operators
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Parameter adaptation strategies
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Coevolutionary techniques
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Multi-criteria optimization
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Parallel calculations
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Hybridization of algorithms
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Convergence analysis
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Visualize the results
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Implementation in a production environment
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Applications in machine learning
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Best implementation practices
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Arch Linux system architecture
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Installation and configuration process
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Package and dependency management
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System administration
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Security and monitoring
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Backup and recovery
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Task automation
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Troubleshooting
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Performance optimization
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Security audit
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Administrative best practices
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WCAG 2.1 standard and its components
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Principles of universal design
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Accessibility implementation techniques
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Accessibility testing tools
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Legal regulations and formal requirements
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Needs of different user groups
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Accessibility audit methodology
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Optimization of interfaces
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Accessibility documentation
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Compliance assurance processes
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Best implementation practices
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Monitoring and maintenance of standards
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Review and approval processes for changes
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Managing comments and discussions
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Guide styles and documentation standards
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Synchronization of changes in the team
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Integration and implementation
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Continuous Integration at SwaggerHub
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Export and publication of documentation
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Mocking and testing APIs
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Monitoring changes and notifications
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SwaggerHub platform architecture and capabilities
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Managing the organization and teams
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Version control of API specifications
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Review and approval processes for changes
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Automation of API documentation
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Guide styles and documentation standards
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Integration with CI/CD systems
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Mocking and testing APIs
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Export and publication of documentation
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Monitoring and tracking changes
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Security and entitlements
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Teamwork at SwaggerHub
Benefits
- An in-depth understanding of the mechanisms of genetic algorithms will enable the design of efficient optimization solutions
- Practical experience in the implementation of genetic operators will enable the adaptation of algorithms to specific design requirements
- Knowledge of parameter adaptation techniques will increase the efficiency of the optimization process
- Knowledge of hybrid methods will contribute to the development of more advanced computational solutions
- The ability to implement parallel computing will improve the processing of large data sets
- Experience in analyzing results will allow better interpretation and evaluation of the obtained solutions.Practical knowledge of Arch Linux system will allow effective management of the server environment
- Of the system philosophy and architecture will enable the creation of optimized configurations
- Experience in installation and configuration will accelerate the deployment of new systems
- Knowledge of package management will increase flexibility in customizing the environment
- Troubleshooting skills will streamline the system maintenance process
- Knowledge of administrative tools will contribute to more efficient infrastructure management.Practical knowledge of the WCAG 2.1 standard will enable the creation of accessible digital interfaces
- The needs of different user groups will allow designing more inclusive solutions
- Knowledge of accessibility testing will streamline the process of verifying compliance with legal requirements
- Experience in implementing WCAG guidelines will accelerate adaptation of existing systems
- The ability to identify and resolve accessibility issues will increase the quality of solutions created
- Knowledge of digital accessibility best practices will contribute to building better user interfaces.Practical knowledge of the SwaggerHub platform will enable more efficient management of API documentation in the organization
- Experience with team collaboration processes will accelerate the development of API projects
- Familiarity with SwaggerHub's version control mechanisms will enable better tracking of changes to specifications
- The ability to configure automation in the platform will increase team efficiency
- Efficient management of permissions and organization in SwaggerHub will improve the security of the API development process
- The ability to integrate with external tools will streamline CI/CD processes
Who is this training for?
Prerequisites
- Solid knowledge of object-oriented programming
- Fundamentals of mathematics and statistics
- Understand the basics of optimization algorithms
- Knowledge of machine learning concepts
- Basic knowledge of Linux systems
- Terminal experience
- Understand the basics of computer networks
- Knowledge of basic system commands
- Basic knowledge of HTML and CSS
- Experience in interface development
- Understand the basics of UX/UI
- Knowledge of the software development process
- Basic knowledge of OpenAPI/Swagger specifications
- Experience working in a team on code
- Knowledge of the basics of version control systems
- Understanding CI/CD processes
Training program
The theory of evolution in a computational context
- Genetic representation design
- Implementation of genetic operators
- Selection and reproduction strategies
- Advanced evolutionary techniques
- Adaptation of algorithm parameters
Coevolutionary techniques
- Multi-population algorithms
Multi-criteria optimization
- Implementation and optimization
Adaptation function design
- Strategies for preserving diversity
- Parallel evolutionary computing
- Hybridization with other methods
Practical applications
- Optimization of combinatorial problems
- Evolution of artificial neural networks
Teaching with reinforcement
- Analysis and visualization of results
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
Who is the Genetic algorithms - advanced techniques training for?
This training is designed for professionals looking to develop skills in genetic algorithms - advanced techniques. Required level: advanced.
How long is the Genetic algorithms - advanced techniques training?
The training lasts 4. Available in online or on-site format.
Will I receive a certificate?
Yes — every participant receives a completion certificate confirming acquired competencies. EITT holds ISO 9001 accreditation.
Can this training be conducted for a closed group?
Yes — we offer dedicated closed trainings for companies. We customize the program to your team's needs. Contact us for an individual quote.
Request a quote
Funding Options
Check funding options for your company
Development Services Database
Up to 80% funding for SMEs from EU funds
Check availabilityNational Training Fund
Up to 100% funding for employers
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Interested in this training?
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