Michael de la Maza, PhD

Greater Boston Contact Info
21K followers 500+ connections

Join to view profile

About

Professor of Business Analytics and Machine Learning @ Hult. I teach Machine Learning in…

Articles by Michael

See all articles

Contributions

Activity

Join now to see all activity

Experience & Education

  • Hult International Business School

View Michael’s full experience

See their title, tenure and more.

or

By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.

Licenses & Certifications

Volunteer Experience

  • Boston Blockchain Association (BBA) Graphic

    Facilitator

    Boston Blockchain Association (BBA)

    - Present 1 year 3 months

    I proposed and created a reading group for the Boston Blockchain Association. I facilitate the bimonthly sessions during which we discuss books, reports, etc. that pertain to blockchain and crypto issues.

Publications

  • An analysis of selection procedures with particular attention paid to proportional and Boltzmann selection

    Proceedings of the fifth international conference on genetic algorithms

  • Dynamic Hill Climbing: Overcoming the limitations of optimization techniques

    Second Turkish Symposium

    This paper describes a novel search algorithm, called dynamic hill climbing, that borrows ideas from genetic algorithms and hill climbing techniques. Unlike both genetic
    and hill climbing algorithms, dynamic hill climbing has the ability to dynamically
    change its coordinate frame during the course of an optimization. Furthermore, the
    algorithm moves from a coarse-grained search to a ne-grained search of the function
    space by changing its mutation rate and uses a diversity-based…

    This paper describes a novel search algorithm, called dynamic hill climbing, that borrows ideas from genetic algorithms and hill climbing techniques. Unlike both genetic
    and hill climbing algorithms, dynamic hill climbing has the ability to dynamically
    change its coordinate frame during the course of an optimization. Furthermore, the
    algorithm moves from a coarse-grained search to a ne-grained search of the function
    space by changing its mutation rate and uses a diversity-based distance metric to ensure that it searches new regions of the space. Dynamic hill climbing is empirically
    compared to a traditional genetic algorithm using De Jong's well-known ve function
    test suite [4] and is shown to vastly surpass the performance of the genetic algorithm,
    often nding better solutions using only 1% as many function evaluations.

    Other authors
    • Deniz Yuret
    See publication

Organizations

  • Scrum Alliance

    -

Recommendations received

More activity by Michael

View Michael’s full profile

  • See who you know in common
  • Get introduced
  • Contact Michael directly
Join to view full profile

Other similar profiles

Explore collaborative articles

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.

Explore More

Add new skills with these courses