Abstract
This work aims to study and explore the use of Gene Expression Programming (GEP) in solving 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 a previous work performed using Genetic Programming.
Highlights
Artificial intelligence has gone a long way in solving various optimization problems; its tools have had an immense role in finding good and acceptable solutions for such problems
In order to evaluate the significance of this work, it is compared with that used by Burke, Hyde and Kendall [5] ; the same data set and similar parameter setting are employed to be used in the comparison
Four datasets were used from Falkenauer [9] as already stated; these four include varying number of pieces to be filled in the bins, which, in their turn, are fixed in their capacity to 150
Summary
Artificial intelligence has gone a long way in solving various optimization problems; its tools have had an immense role in finding good and acceptable solutions for such problems. Methods that seek solutions for such problems usually intend to find the best packing strategy by determining the best way to pack the items so that they all fit into a minimum number of storage bins. The application of this sort of problem to real world situations can be of a great practical significance, and it can be found in many appropriate areas such as transportation, production, industrial regions and many more. Bin-Packing is of an essential theoretical importance, being used as an early proving base for many of the classical approaches to analyze the performance of approximation algorithms [7]
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