  This work aims to study and explore the use of Gene Expression Programming
(GEP) in solving the on-line Bin-Packing problem. The main idea is to show how
GEP can automatically find acceptable heuristic rules to solve the problem
efficiently and economically. One dimensional Bin-Packing problem is considered
in the course of this work with the constraint of minimizing the number of bins
filled with the given pieces. Experimental Data includes instances of benchmark
test data taken from Falkenauer (1996) for One-dimensional Bin-Packing
Problems. Results show that GEP can be used as a very powerful and flexible
tool for finding interesting compact rules suited for the problem. The impact
of functions is also investigated to show how they can affect and influence the
success of rates when they appear in rules. High success rates are gained with
smaller population size and fewer generations compared to previous work
performed using Genetic Programming.
