Evolving reusable 3d packing heuristics with genetic programming

Allen, Sam D.; Burke, Edmund K.; Hyde, Matthew; Kendall, Graham

doi:10.1145/1569901.1570029

Cited by 29 publications

(20 citation statements)

References 19 publications

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“…This work was extended to three dimensional bin packing by Allen at al. [20] and generalised by Burke et al [6] to include one, two and three dimensional bin packing problems, again obtaining human competitive results. A similar method was presented by Burke et al [21] for two dimensional strip packing problems.…”

Section: B Genetic Programming As a Hyper-heuristicmentioning

confidence: 92%

A genetic programming hyper-heuristic for the multidimensional knapsack problem

et al. 2014

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Abstract-Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. Early hyperheuristics focussed on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. This work investigates the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results.

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Section: B Genetic Programming As a Hyper-heuristicmentioning

confidence: 92%

A genetic programming hyper-heuristic for the multidimensional knapsack problem

et al. 2014

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“…In the domain of production scheduling, genetic programming has been used to evolve dispatching rules. Genetic programming has also been successfully applied to produce heuristics for one-dimensional bin packing [33], twodimensional strip packing [34] and three-dimensional knapsack packing [35]. In the domain of satisfiability, Fukunaga describes CLASS (Composite Heuristic Learning Algorithm for SAT Search), an automated genetic programming heuristic discovery system for SAT.…”

Section: A Hyper-heuristics For Heuristic Generationmentioning

confidence: 99%

A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction

Ortíz-Bayliss

Özcan

Parkes

et al. 2013

2013 13th UK Workshop on Computational Intelligence (UKCI)

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Abstract-A constraint satisfaction problem (CSP) is a combinatorial optimisation problem with many real world applications. One of the key aspects to consider when solving a CSP is the order in which the variables are selected to be instantiated. In this study, we describe a genetic programming hyper-heuristic approach to automatically produce heuristics for CSPs. Human-designed 'standard' heuristics are used as components enabling the construction of new variable ordering heuristics which is achieved through the proposed approach. We present empirical evidence that the heuristics produced by our approach are competitive considering the cost of the search when compared to the standard heuristics which are used to obtain the components for the new heuristics. The proposed approach is able to produce specialized heuristics for specific classes of instances that outperform the best standard heuristics for the same instances.

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“…In that work, the heuristics were expressions, which provided a score to each available bin. The GP system utilised the +, −, * , and % (protected divide, see [1]) operators, and the three terminals available to the GP were the piece size, the bin fullness, and the bin capacity. The current piece is put into the bin which received the highest score.…”

Section: Previous Workmentioning

confidence: 99%

“…One example is when GP is used to generate heuristic functions, which give a score to a number of options at any given decision point (see [1,5,3,[7][8][9] for examples on many different problems, including job shop scheduling, cutting/packing, and SAT). This is equivalent to an 'index policy' [10], because each potential option is given a score independently of other options, and the option with the largest score is selected.…”

Section: Introductionmentioning

confidence: 99%

Matrix Analysis of Genetic Programming Mutation

Parkes

Özcan

Hyde

2012

Lecture Notes in Computer Science

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Abstract. Heuristic policies for combinatorial optimisation problems can be found by using Genetic programming (GP) to evolve a mathematical function over variables given by the current state of the problem, and whose value is used to determine action choices (such as preferred assignments or branches). If all variables have finite discrete domains, then the expressions can be converted to an equivalent lookup table or 'decision matrix'. Spaces of such matrices often have natural distance metrics (after conversion to a standard form). As a case study, and to support the understanding of GP as a meta-heuristic, we extend previous bin-packing work and compare the distances between matrices from before and after a GP-driven mutation. We find that GP mutations often correspond to large moves within the space of decision matrices. This gives evidence that the role of mutations within GP might be somewhat different than their role within Genetic Algorithms.

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Evolving reusable 3d packing heuristics with genetic programming

Cited by 29 publications

References 19 publications

A genetic programming hyper-heuristic for the multidimensional knapsack problem

A genetic programming hyper-heuristic for the multidimensional knapsack problem

A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction

Matrix Analysis of Genetic Programming Mutation

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