A new evolutionary approach to cutting stock problems with and without contiguity

  • Authors:

  • Ko-Hsin Liang;Xin Yao;Charles Newton;David Hoffman

  • Affiliations:

  • School of Computer Science, University College, The University of New South Wales, Australian Defence Force Academy, Canberra, ACT 2600, Australia;School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;School of Computer Science, University College, The University of New South Wales, Australian Defence Force Academy, Canberra, ACT 2600, Australia;School of Computer Science, University College, The University of New South Wales, Australian Defence Force Academy, Canberra, ACT 2600, Australia

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2002

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Abstract

Evolutionary algorithms (EAs) have been applied to many optimization problems successfully in recent years. The genetic algorithm (GAs) and evolutionary programming (EP) are two different types of EAs. GAs use crossover as the primary search operator and mutation as a background operator, while EP uses mutation as the primary search operator and does not employ any crossover. This paper proposes a novel EP algorithm for cutting stock problems with and without contiguity. Two new mutation operators are proposed. Experimental studies have been carried out to examine the effectiveness of the EP algorithm. They show that EP can provide a simple yet more effective alternative to GAs in solving cutting stock problems with and without contiguity. The solutions found by EP are significantly better (in most cases) than or comparable to those found by GAs.