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Cómputo evolutivo
This course is part of Conceptos Básicos de Inteligencia Artificial.
Course Cost
Free course
Intermediate
Skill Level
17 Hours
Self-paced lessons
This comprehensive course explores evolutionary computation (EC), a field that applies natural evolution and genetic theories to computational structures. Students will learn how evolutionary algorithms provide alternative approaches to solving complex problems across diverse fields including engineering, economics, medicine, and arts. The curriculum begins with fundamental concepts of genetic algorithms and evolutionary computation, then progresses to practical implementation and applications. You'll master genetic operators like crossover and mutation, understand the schema theorem, and learn to formulate decision variables for various problem domains. The course also covers other bio-inspired optimization techniques like particle swarm optimization and differential evolution, providing a well-rounded understanding of nature-inspired algorithms for optimization and search problems.
4,170 Enrolled
Spanish
What you'll learn
Understand the fundamental principles of evolutionary computation and genetic algorithms
Formulate and identify decision variables for optimization problems across various domains
Implement basic genetic algorithms with appropriate selection, crossover, and mutation operators
Analyze algorithm performance using the schema theorem
Compare different encoding strategies for genetic algorithms
Solve the Traveling Salesman Problem using evolutionary approaches
Apply genetic algorithms to practical optimization challenges
Understand alternative bio-inspired optimization techniques
Skills you'll gain
This course includes:
1.4 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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Share your certificate with prospective employers and your professional network on LinkedIn.
CREATED BY
PROVIDED BY
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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
The Cómputo evolutivo (Evolutionary Computing) course provides a comprehensive introduction to evolutionary algorithms and their applications in solving complex optimization and search problems. The curriculum begins with foundational concepts of evolutionary computation, explaining how these methods are inspired by natural evolution processes including selection, crossover, and mutation. Students learn the principles of genetic algorithms, starting with theoretical foundations like the schema theorem and progressing to practical implementation details. The course covers formulation of optimization problems, selection of appropriate genetic operators and parameters, and analysis of algorithm performance. Advanced topics include specialized encodings for different problem domains, with particular focus on combinatorial optimization problems like the Traveling Salesman Problem. The final module introduces other bio-inspired techniques such as particle swarm optimization and differential evolution, providing a broader perspective on nature-inspired computing approaches.
Introducción a la computación evolutiva
Module 1 · 2 Hours to complete
Principios de operación de un algoritmo genético
Module 2 · 6 Hours to complete
Implementación de un algoritmo genético básico
Module 3 · 4 Hours to complete
Aplicaciones de algoritmos genéticos y otras técnicas evolutivas
Module 4 · 5 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Conceptos Básicos de Inteligencia Artificial
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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