Changing Representations During Search: A Comparative Study of Delta Coding

1 September 1994

journal article
Published by MIT Press in Evolutionary Computation

Vol. 2 (3) , 249-278
https://doi.org/10.1162/evco.1994.2.3.249

Abstract

Delta coding is an iterative genetic search strategy that dynamically changes the representation of the search space in an attempt to exploit different problem representations. Delta coding sustains search by reinitializing the population at each iteration of search. This helps to avoid the asymptotic performance typically observed in genetic search as the population becomes more homogeneous. Here, the optimization ability of delta coding is empirically compared against CHC, ESGA, GENITOR, and random mutation hill-climbing (RMHC) on a suite of well-known test functions with and without Gray coding. Issues concerning the effects of Gray coding on these test functions are addressed.

Keywords

This publication has 3 references indexed in Scilit:

A genetic algorithm tutorial
Statistics and Computing, 1994
Dynamic Parameter Encoding for genetic algorithms
Machine Learning, 1992
Processor Allocation in an N-Cube Multiprocessor Using Gray Codes
IEEE Transactions on Computers, 1987