Evolving Bin Packing Heuristics with Genetic Programming

Burke, Edmund K.; Hyde, Matthew; Kendall, Graham

doi:10.1007/11844297_87

Cited by 105 publications

(80 citation statements)

References 16 publications

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“…This representational issue might well explain why previous work by Burke et al (2006) was only able to equal the performance of standard heuristics whereas we significantly outperform them. However, we have only demonstrated that some of the best matrices can be 'spiky' ('rough').…”

Section: Analysis Of Genetic Algorithms For Policy Matrix Generationmentioning

confidence: 77%

“…The genetic programming approach as provided in (Burke et al, 2006(Burke et al, , 2007b and human designed policies for online bin packing implicitly make the assumption that 'cleanly structured nice' policies are the best solutions. However, evidence presented by this work shows that this does need not to be true in general.…”

Section: Resultsmentioning

confidence: 99%

“…GP has been applied to many different challenging problems (Poli et al, 2008). Sim and Hart (2013) A particularly relevant study is provided in Burke et al (2006). The genetic programming approach is used to evolve heuristics to decide which bin to place a given item.…”

Section: Hyper-heuristics For Bin Packingmentioning

confidence: 99%

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CHAMP: Creating heuristics via many parameters for online bin packing

Asta

Özcan

Parkes

2016

Expert Systems with Applications

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The online bin packing problem is a well-known bin packing variant which requires immediate decisions to be made for the placement of a lengthy sequence of arriving items of various sizes one at a time into fixed capacity bins without any overflow. The overall goal is maximising the average bin fullness. We investigate a 'policy matrix' representation which assigns a score for each decision option independently and the option with the highest value is chosen for one dimensional online bin packing. A policy matrix might also be considered as a heuristic with many parameters, where each parameter value is a score.We hence investigate a framework which can be used for creating heuristics via many parameters. The proposed framework combines a Genetic Algorithm optimiser, which searches the space of heuristics in policy matrix form, and an online bin packing simulator, which acts as the evaluation function. The empirical results indicate the success of the proposed approach, providing the best solutions for almost all item sequence generators used during the experiments.We also present a novel fitness landscape analysis on the search space of policies. This study hence gives evidence of the potential for automated discovery by intelligent systems of powerful heuristics for online problems; reducing the need for expensive use of human expertise.

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Section: Analysis Of Genetic Algorithms For Policy Matrix Generationmentioning

confidence: 77%

Section: Resultsmentioning

confidence: 99%

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CHAMP: Creating heuristics via many parameters for online bin packing

Asta

Özcan

Parkes

2016

Expert Systems with Applications

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“…For example, GP has evolved competitive SAT solvers [1,2,14,23], state-of-the-art or better than stateof-the-art bin packing algorithms [4,40], particle swarm optimisers [39], evolutionary algorithms [31], and TSP solvers [22,32].…”

Section: Genetic Programming and Hyper-heuristicsmentioning

confidence: 99%

A Hyper-Heuristic Approach to Evolving Algorithms for Bandwidth Reduction Based on Genetic Programming

Koohestani

Poli

2011

Research and Development in Intelligent Systems XXVIII

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The bandwidth reduction problem is a well-known NP-complete graphlayout problem that consists of labeling the vertices of a graph with integer labels in such a way as to minimize the maximum absolute difference between the labels of adjacent vertices. The problem is isomorphic to the important problem of reordering the rows and columns of a symmetric matrix so that its non-zero entries are maximally close to the main diagonal -a problem which presents itself in a large number of domains in science and engineering. A considerable number of methods have been developed to reduce the bandwidth, among which graph-theoretic approaches are typically faster and more effective. In this paper, a hyper-heuristic approach based on genetic programming is presented for evolving graph-theoretic bandwidth reduction algorithms. The algorithms generated from our hyper-heuristic are extremely effective. We test the best of such evolved algorithms on a large set of standard benchmarks from the Harwell-Boeing sparse matrix collection against two state-of-the-art algorithms from the literature. Our algorithm outperforms both algorithms by a significant margin, clearly indicating the promise of the approach.

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“…Studying novel approaches for the development of hyper-heuristics is important since they are domain-independent problem strategies that operate on a space of heuristics, rather than on a space of solutions, and rise the level of generality on automated problem solving. Hyper-heuristics have been employed for solving search and optimisation problems such as bin-packing [4,17], timetabling [14], scheduling [8,9] and satisfiability [2] among others. For detailed reviews of hyper-heuristics and their applications, please refer to [7,13,16].…”

Section: Heuristics Designmentioning

confidence: 99%

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

Terrazas

Landa-Silva

Krasnogor

2010

Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)

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The current research trends on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for specific problems, that is, the input to the algorithm are problems and the output are problem-tailored heuristics. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Thus, hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem in hand. Some approaches like genetic programming have been proposed for this. In this paper, we report on an alternative methodology that sheds light on simple methodologies that efficiently cooperate by means of local interactions. These entities are seen as building blocks, the combination of which is employed for the automated manufacture of good performing heuristic search strategies. We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem.

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Evolving Bin Packing Heuristics with Genetic Programming

Cited by 105 publications

References 16 publications

CHAMP: Creating heuristics via many parameters for online bin packing

CHAMP: Creating heuristics via many parameters for online bin packing

A Hyper-Heuristic Approach to Evolving Algorithms for Bandwidth Reduction Based on Genetic Programming

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

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