Lossless fitness inheritance in genetic algorithms for decision trees
نویسندگان
چکیده
منابع مشابه
Lossless fitness inheritance in genetic algorithms for decision trees
When genetic algorithms are used to evolve decision trees, key tree quality parameters can be recursively computed and re-used across generations of partially similar decision trees. Simply storing instance indices at leaves is enough for fitness to be piecewise computed in a lossless fashion. We show the derivation of the (substantial) expected speed-up on two bounding case problems and trace ...
متن کاملConstructing Binary Decision Trees using Genetic Algorithms
Decision trees have been well studied and widely used in knowledge discovery and decision support systems. Although the problem of finding an optimal decision tree has received attention, it is a hard optimization problem. Here we propose utilizing a genetic algorithm to improve on the finding of ap-propriate decision trees. We present a method to encode/decode a decision tree to/from a chromo-...
متن کاملRepresenting Trees in Genetic Algorithms
| We consider the problem of representing trees (undirected, cycle-free graphs) in Genetic Algorithms. This problem arises, among other places, in the solution of network design problems. After comparing several commonly used representations based on their usefulness in genetic algorithms, we describe a new representation and show it to be superior in almost all respects to the others. In parti...
متن کاملOn Greedy Algorithms for Decision Trees
In the general search problem we want to identify a specific element using a set of allowed tests. The general goal is to minimize the number of tests performed, although different measures are used to capture this goal. In this work we introduce a novel greedy approach that achieves the best known approximation ratios simultaneously for many different variations of this identification problem....
متن کاملFitness Interpolation in Interactive Genetic Algorithms
We attack the problem of user fatigue in using an interactive genetic algorithm to evolve two case studies: user interfaces and floorplans. We show that we can reduce human fatigue in interactive genetic algorithms (the number of choices needing to be made by the user), by 1) only asking the user to evaluate a small subset from a large population size, and 2) by asking the user to make the choi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Soft Computing
سال: 2009
ISSN: 1432-7643,1433-7479
DOI: 10.1007/s00500-009-0489-y