By Topic

Decision making in a hybrid genetic algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)

There are several issues that need to be taken into consideration when designing a hybrid problem solver. The paper focuses on one of them-decision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selecto-recombinative GA with a simple hill climber (HC). Tests on an easy problem with a small population size match our intuition that both GA and HC are needed to solve the problem efficiently

Published in:

Evolutionary Computation, 1997., IEEE International Conference on

Date of Conference:

13-16 Apr 1997