1 / 9

Genetic Algorithms -- System Modeling and Function Finding

Genetic Algorithms -- System Modeling and Function Finding. Jack Perdue CPSC 689-608 March 21, 2002. Problem Statement.

shelby
Télécharger la présentation

Genetic Algorithms -- System Modeling and Function Finding

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Genetic Algorithms -- System Modeling and Function Finding Jack Perdue CPSC 689-608 March 21, 2002 Genetic Algorithms -- System Modeling and Function Finding

  2. Problem Statement • Given a set of observed metrics (variables) where some metrics are dependent upon others (but the relationship is unclear), develop a function/model for the the dependent metrics. Genetic Algorithms -- System Modeling and Function Finding

  3. Real-World Examples • Predicting freeway speed given the number of cars entering and exiting, the day of the week, time of day and weather conditions. • Predicting the winner of a sports event given statistical history of teams/individuals involved. • Predicting how long a program will run on a particular (parallel) computer system given a history of its past performance and impact on the system. Genetic Algorithms -- System Modeling and Function Finding

  4. A Simple Example Here we have some “observed” metrics. It is instantly apparent that the function here is something along the lines of: x * y = 36 or y = 36 / x Genetic Algorithms -- System Modeling and Function Finding

  5. A Harder Example Here we have some “observed” metrics. If we were to plot it, we would see it is a circle, but it isn’t apparent just looking at the numbers. (x-1)2 + (y-2)2 = 32 or y = 2 ± sqrt(32 - (x-1)2) Genetic Algorithms -- System Modeling and Function Finding

  6. Higher dimensions Although we can visualize and conceptualize functions of two or three variables, the human mind’s ability to deduce patterns is decreased as the dimensionality is increased. We have methods such as linear least squares (for example) to help us with larger dimensions, but given a large volume of observed metrics, they often are computationally prohibitive. Genetic Algorithms -- System Modeling and Function Finding

  7. Enter GA/GP/GEPs As we have been learning the past few weeks, when we have a large problem space and are trying to find a needle in a haystack (or something resembling a needle), then the field of genetic algorithms and programming may be of help. Genetic Algorithms -- System Modeling and Function Finding

  8. GAs vs. GP vs. GEP(according to Candida Ferreira) • GAs - individuals are linear strings of fixed length (whole string considered) • GP - individuals non-linear entities of different sizes and shapes (parse trees) • GEP - individuals are encoded and linear strings of fixed length but interpreted as non-linear entites of different sizes and shapes (partial strings considered) Genetic Algorithms -- System Modeling and Function Finding

  9. So, what is Gene Expression Programming (GEP) ... on to Candida Ferreira’s tutorial... Genetic Algorithms -- System Modeling and Function Finding

More Related