Evolutionary Artificial Intelligence: An Industrial Case Study

Duration: "2 hours"
Start: "TBA"
Location: "TBA"

Abstract:
This tutorial introduces the fundamentals and applications of evolutionary artificial intelligence (EAI), emphasizing its use in solving complex industrial problems. Through a detailed case study, participants will explore how evolutionary algorithms can be integrated into real-world production environments to optimize decision-making processes. The tutorial blends theory and hands-on insights, guiding attendees through the design, adaptation, and deployment of metaheuristic algorithms in industry-relevant scenarios.

Detailed Outline

  • Introduction to Evolutionary Artificial Intelligence: Overview of EAI, its foundations, and relevance in solving NP-hard problems in industrial contexts.
  • Metaheuristics and Problem Solving: Explanation of genetic algorithms and their role in tackling combinatorial optimization problems.
  • Problem Formulation in Industry: Understanding how industrial problems (e.g., scheduling, bin packing, routing) are modeled as optimization problems.
  • Case Study Overview: Presentation of an industrial case study involving the design and application of a grouping genetic algorithm for an unrelated parallel machine scheduling problem.
  • Design of the Evolutionary Approach: Step-by-step explanation of the algorithm design: representation, selection, crossover, mutation, rearrangement, and evaluation.
  • Experimentation and Analysis: Description of the experimental setup, performance evaluation, instance feature analysis, and explainability of results using data analysis techniques.
  • Deployment and Integration: Challenges and strategies for transferring AI solutions into operational industrial systems.
  • Discussion and Future Directions: Open problems, trends in automated algorithm design, and integration with data science pipelines.

    • Target Audience and Prerequisites:
    This tutorial is intended for researchers, graduate students, and industry practitioners interested in artificial intelligence, metaheuristics, and their practical applications. Participants are expected to have a basic understanding of optimization problems, algorithms, and Python programming. Familiarity with evolutionary computation is a plus but not required.
    Contact:
    Dra. Marcela Quiroz Castellanos maquiroz@uv.mx
    Dr. Octavio Ramos Figueroa oivatco.rafo@mail.com


    Dra. Marcela Quiroz Castellanos
    Marcela Quiroz is a Full-Time Researcher with the Artificial Intelligence Research Institute at the Universidad Veracruzana in Xalapa City, Mexico. Her research interests include: combinatorial optimization, metaheuristics, experimental algorithms, characterization and data mining. She received her Ph.D. in Computer Science from the Instituto Tecnologico de Tijuana, Mexico. She studied engineering in computer systems and received the degree of master in computer science at the Instituto Tecnológico de Ciudad Madero, Mexico. She is a member of the Mexican National Researchers System (SNI), and also a member of the directive committees of the Mexican Computing Academy (AMexComp) and the Mexican Robotics Federation (FMR).
    Dr. Octavio Ramos Figueroa
    Octavio Ramos-Figueroa is a postdoctoral researcher at the Artificial Intelligence Research Institute of the Universidad Veracruzana, specializing in evolutionary computation, data-driven algorithm design, and metaheuristics for NP-hard optimization problems. He received his Ph.D. and master’s degree in Artificial Intelligence from the Artificial Intelligence Research Institute at the Universidad Veracruzana, Mexico. He studied engineering in information and communications technologies at the Instituto Tecnológico de Tepic, Mexico. His research interests include continuous and combinatorial optimization, experimental study of metaheuristic and hyper heuristic algorithms, characterization, data mining, and data science pipelines. He is a member of the Mexican National Researchers System (SNI), the Mexican Computing Academy (AMexComp), and the Mexican Society of Operations Research (SMIO).