By Topic

A new methodology for reducing brittleness in genetic programming

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
$31 $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)
Moore, F.W. ; Wright State Univ., Dayton, OH, USA ; Garcia, O.N.

Genetic programming systems typically use a fixed training population to optimize programs according to problem-specific fitness criteria. The best-of-run programs evolved by these systems frequently exhibit optimal (or near-optimal) performance in competitive survival environments explicitly represented by the training population. Unfortunately, subsequent performance of these programs is often less than optimal when situations arise that were not explicitly anticipated during program evolution. This paper describes a new methodology which promises to reduce the brittleness of best-of-run programs evolved by genetic programming systems. Instead of using a fixed set of fitness cases, the new methodology creates a new set of randomly-generated fitness cases prior to the evaluation of each generation of the evolutionary process. A genetic programming system that evolves optimized maneuvers for an extended 2D pursuer/evader problem was modified for this study. The extended 2D pursuer/evader problem is a competitive zero-sum game in which an evader attempts to escape a faster, more agile pursuer by performing specific combinations of thrusting and turning maneuvers. The pursuer uses the highly effective proportional navigation algorithm to control its trajectory towards the evader. The original genetic programming system used a fixed training set of pursuers. Each of these pursuers was uniquely identified by two parameters: the initial distance from pursuer to evader, and the angle that the velocity vector of the evader makes relative to the pursuer/evader line-of-sight at the time the pursuer is launched. The modified system implemented for this project was identical to the original system, except that it used random distances and angles to create a new set of fitness cases prior to each generation of the genetic programming run. Best-of-run programs were independently evolved using fixed and randomly-generated fitness cases. These programs were subsequently tested against a large, representative fixed population of pursuers to determine their relative effectiveness. This paper describes the implementation of both the original and modified systems, and summarizes the results of these tests

Published in:

Aerospace and Electronics Conference, 1997. NAECON 1997., Proceedings of the IEEE 1997 National  (Volume:2 )

Date of Conference:

14-18 Jul 1997