Generalizing Hyper-heuristics via Apprenticeship Learning

2015

Information Sciences

Self Cite

Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance methods under an iterative single point-based search framework. At each step, the solution in hand is modified after applying a selected heuristic and a decision is made whether the new solution is accepted or not. In this study, we represent the trail of a hyper-heuristic as a third order tensor. Factorization of such a tensor reveals the latent relationships between the low level heuristics and the hyper-heuristic itself. The proposed learning approach partitions the set of low level heuristics into two equal subsets where heuristics in each subset are associated with a separate move acceptance method. Then a multi-stage hyper-heuristic is formed and while solving a given problem instance, heuristics are allowed to operate only in conjunction with the associated acceptance method at each stage. To the best of our knowledge, this is the first time tensor analysis of the space of heuristics is used as a data science approach to improve the performance of a hyper-heuristic in the prescribed manner. The empirical results across six different problem domains from a benchmark indeed indicate the success of the proposed approach.

Section: Discussionmentioning

confidence: 99%

A tensor-based selection hyper-heuristic for cross-domain heuristic search

2015

Information Sciences

Self Cite

“…In a previous work, [12], apprenticeship learning (AL) technique was used to generalize hyper-heuristics in the Bin-Packing domain. The AL method has a wide range of applications in control and robotics and is heavily based on Inverse Reinforcement Learning (IRL).…”

Section: Related Workmentioning

confidence: 99%

“…The classifier produced from this dataset is used to predict the best action at a given search state while dealing with an unseen problem instance. The ALbased approach in [12] was trained on small problem instances and was capable of generalizing the extracted knowledge to larger problem instances. The major drawback of the approach proposed in [12] was that the definition of the search state was problem dependent.…”

Section: Related Workmentioning

confidence: 99%

An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

2014

Self Cite

Abstract-Apprenticeship learning occurs via observations while an expert is in action. A hyper-heuristic is a search method or a learning mechanism that controls a set of low level heuristics or combines different heuristic components to generate heuristics for solving a given computationally hard problem. In this study, we investigate into a novel apprenticeshiplearning-based approach which is used to automatically generate a hyper-heuristic for vehicle routing. This approach itself can be considered as a hyper-heuristic which operates in a train and test fashion. A state-of-the-art hyper-heuristic is chosen as an expert which is the winner of a previous hyper-heuristic competition. Trained on small vehicle routing instances, the learning approach yields various classifiers, each capturing different actions that the expert hyper-heuristic performs during the search process. Those classifiers are then used to produce a hyper-heuristic which is potentially capable of generalizing the actions of the expert hyperheuristic while solving the unseen instances. The experimental results on vehicle routing using the Hyper-heuristic Flexible (HyFlex) framework shows that the apprenticeship-learningbased hyper-heuristic delivers an outstanding performance when compared to the expert and some other previously proposed hyper-heuristics.

“…This could be useful in a variety of applications (i.e. life-long learning as in (Silver et al, 2013), and or apprenticeship learning as in (Asta et al, 2013) and (Asta and Ozcan, 2014)). Finally, the framework here is different than the one proposed in (Asta andÖzcan, 2015) when parameter control for each low level heuristic is considered.…”

Section: Introductionmentioning

confidence: 99%

A tensor based hyper-heuristic for nurse rostering

Knowledge-Based Systems

Curtois

2016

Self Cite

Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly preferred as solution methods which are often designed and tuned for specific (group of) problem instances. Hyper-heuristics have emerged as general search methodologies that mix and manage a predefined set of low level heuristics while solving computationally hard problems. In this study, we describe an online learning hyper-heuristic employing a data science technique which is capable of self-improvement via tensor analysis for nurse rostering. The proposed approach is evaluated on a well-known nurse rostering benchmark consisting of a diverse collection of instances obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four of the instances.