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Greedy Seeding Procedure for GAs Solving a Strip Packing Problem

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TLDR
In this paper, a knowledge-based greedy seeding procedure was used for creating the initial population, motivated by the expectation that the seeding will speed up the GA by starting the search in promising regions of the search space.
Abstract
In this paper, the two-dimensional strip packing problem with 3-stage level patterns is tackled using genetic algorithms (GAs). We evaluate the usefulness of a knowledge-based greedy seeding procedure used for creating the initial population. This is motivated by the expectation that the seeding will speed up the GA by starting the search in promising regions of the search space. An analysis of the impact of the seeded initial population is offered, together with a complete study of the influence of these modifications on the genetic search. The results show that the use of an appropriate seeding of the initial population outperforms existing GA approaches on all the used problem instances, for all the metrics used, and in fact it represents the new state of the art for this problem.

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Journal ArticleDOI

A survey on heuristics for the two-dimensional rectangular strip packing problem

TL;DR: In this article, a survey of two-dimensional rectangular strip packing problems is presented, in which all items are rectangles, therefore fully characterized by a width and a height, and the large object is a strip, i.e. a rectangle with a fixed width but an infinite height.
Journal ArticleDOI

The rectangular two-dimensional strip packing problem real-life practical constraints: A bibliometric overview

TL;DR: An extensive literature review covering scientific publications about the rectangular 2D-SPP constraints is presented in order to provide a useful foundation to support new research works and indicates opportunities to address real-life practical constraints.
References
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Book

Nonparametric statistics for the behavioral sciences

Sidney Siegel
TL;DR: This is the revision of the classic text in the field, adding two new chapters and thoroughly updating all others as discussed by the authors, and the original structure is retained, and the book continues to serve as a combined text/reference.
Book

Genetic Algorithms + Data Structures = Evolution Programs

TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Book

Handbook of Evolutionary Computation

TL;DR: The Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications, intended to become the standard reference resource for the evolutionary computation community.
Book

Genetic Algorithms and Simulated Annealing

TL;DR: A detergent composition mainly for automatic laundering machines which comprises, on the basis of 100 parts by weight of total composition, at least 60 parts of soap and no more than 10 parts of a mixture of surfactants which impart an excellent detergent ability and foam control even in very soft waters and non-polluting properties.
Journal ArticleDOI

A genetic algorithm for flowshop sequencing

TL;DR: A Genetic Algorithm is developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem and the performance of the algorithm is compared with that of a naive Neighbourhood Search technique and with a proven Simulated Annealing algorithm.
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