@inproceedings{47211f1493744b2686390e5b97eab50e,
title = "Solving cutting stock problems by evolutionary programming",
abstract = "Evolutionary algorithms (EAs) have been applied to many optimisation problems successfully in recent years. The genetic algorithm (GA) and evolutionary programming (EP) are two of the major branches of EAs. GAs use crossover as the main search operator and mutation as a background operator in search. EP typically uses mutation only. This paper investigates a novel EP algorithm for cutting stock problems. It adopts a mutation operator based on the concept of distance between a parent and its offspring. Without using crossover, the algorithm is less time consuming and more efficient in comparison with a GA-based approach. Experimental studies have been carried out to examine the effectiveness of the EP algorithm. They illustrate that EP can provide a simple yet more efficient alternative to GAs in solving some combinatorial optimisation problems. {\textcopyright} Springer-Verlag Berlin Heidelberg 1998.",
keywords = "Mutation Operator, Crossover Operator, Combinatorial Optimisation Problem, Finite State Machine, Fitness Evaluation Function",
author = "Ko-Hsin LIANG and Xin YAO and Charles NEWTON and David HOFFRNAN",
year = "1998",
doi = "10.1007/bfb0040826",
language = "English",
isbn = "9783540648918",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "755--764",
editor = "PORTO, {V. W.} and N. SARAVANAN and D. WAAGEN and EIBEN, {A. E.}",
booktitle = "Evolutionary Programming VII : 7th International Conference, EP98, San Diego, California, USA, March 25–27, 1998 Proceedings",
note = "EP98: International Conference on Evolutionary Programming ; Conference date: 25-03-1998 Through 27-03-1998",
}