Accession Number : ADA293964
Title : An Analysis of Hierarchical Genetic Programming.
Descriptive Note : Technical rept.,
Corporate Author : ROCHESTER UNIV NY DEPT OF COMPUTER SCIENCE
Personal Author(s) : Rosca, Justinian P.
PDF Url : ADA293964
Report Date : MAR 1995
Pagination or Media Count : 27
Abstract : Hierarchical genetic programming (HGP) approaches rely on the discovery, modification, and use of new functions to accelerate evolution. This paper provides a qualitative explanation of the improved behavior of HGP, based on an analysis of the evolution process from the dual perspective of diversity and causality. From a static point of view, the use of an HGP approach enables the manipulation of a population of higher diversity programs. Higher diversity increases the exploratory ability of the genetic search process, as demonstrated by theoretical and experimental fitness distributions and expanded structural complexity of individuals. From a dynamic point of view, this report analyzes the causality of the crossover operator. Causality relates changes in the structure of an object with the effect of such changes, i.e. changes in the properties or behavior of the object. The analyses of crossover causality suggests that HGP discovers and exploits useful structures in a bottom-up, hierarchical manner. Diversity and causality are complementary, affecting exploration and exploitation in genetic search. Unlike other machine learning techniques that need extra machinery to control the tradeoff between them, HGP automatically trades off exploration and exploitation. (AN)
Descriptors : *LEARNING MACHINES, *STRUCTURED PROGRAMMING, ALGORITHMS, SCENARIOS, COMPARISON, SOLUTIONS(GENERAL), SEARCHING, EVOLUTION(GENERAL), EXPERT SYSTEMS, EXPANSION, TRADE OFF ANALYSIS, SUBROUTINES, ADAPTATION, DYNAMIC PROGRAMMING, HIERARCHIES, FIELDS(COMPUTER PROGRAMS), CONTROL SEQUENCES, AUTOMATIC PROGRAMMING.
Subject Categories : Computer Programming and Software
Distribution Statement : APPROVED FOR PUBLIC RELEASE