Exploring Hyper-heuristic Methodologies with Genetic Programming

Burke, Edmund K.; Hyde, Mathew R.; Kendall, Graham; Ochoa, Gabriela; Özcan, Ender; Woodward, John R.

doi:10.1007/978-3-642-01799-5_6

Cited by 231 publications

(162 citation statements)

References 42 publications

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“…The most commonly used generation hyper-heuristic in the scientific literature is Genetic Programming (GP) (Burke et al, 2009) which evolves computer programs based on given components. GP has been applied to many different challenging problems (Poli et al, 2008).…”

Section: Hyper-heuristics For Bin Packingmentioning

confidence: 99%

“…Heuristics are usually designed by an expert through a trial and error process particularly for solving the problems from a specific domain. Since the time-consuming nature of the overall process, automated generation of heuristics has been of interest to both academics and practitioners (Ross, 2005;Chakhlevitch and Cowling, 2008;Burke et al, 2009). …”

Section: Introductionmentioning

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: Hyper-heuristics For Bin Packingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CHAMP: Creating heuristics via many parameters for online bin packing

Asta

Özcan

Parkes

2016

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…In Burke et al [22], a general genetic programming-based hyper-heuristic framework was presented and some studies were used to explain the idea. Burke et al [24] provided a general survey of related studies on hyper-heuristics developed to deal with a wide range of scheduling and combinatorial optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

220

101

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Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

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“…The desire for greater automation has led to approaches such as hyper-heuristics [3], which are the application of search to the problem of finding good heuristics ('heuristics for searching the space of heuristics'). Of particular interest for the automated design of algorithms are generative hyper-heuristics [5], which assemble basic components into more complex search algorithms. It is also worth mentioning algorithm portfolios [38], which use a group (portfolio) of different algorithms at the same time to solve a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

Haiku - a Scala Combinator Toolkit for Semi-automated Composition of Metaheuristics

Kocsis

Brownlee

Swan

et al. 2015

Search-Based Software Engineering

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Abstract. There is an emerging trend towards the automated design of metaheuristics at the software component level. In principle, metaheuristics have a relatively clean decomposition, where well-known frameworks such as ILS and EA are parameterised by variant components for acceptance, perturbation etc. Automated generation of these frameworks is not so simple in practice, since the coupling between components may be implementation specific. Compositionality is the ability to freely express a space of designs 'bottom up' in terms of elementary components: previous work in this area has used combinators, a modular and functional approach to componentisation arising from foundational Computer Science. In this article, we describe Haiku, a combinator toolkit written in the Scala language, which builds upon previous work to further automate the process by automatically composing the external dependencies of components. We provide examples of use and give a case study in which a programatically-generated heuristic is applied to the Travelling Salesman Problem within an Evolutionary Strategies framework.

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Exploring Hyper-heuristic Methodologies with Genetic Programming

Cited by 231 publications

References 42 publications

CHAMP: Creating heuristics via many parameters for online bin packing

CHAMP: Creating heuristics via many parameters for online bin packing

Genetic programming for production scheduling: a survey with a unified framework

Haiku - a Scala Combinator Toolkit for Semi-automated Composition of Metaheuristics

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