A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex

Evolutionary Computation

2021

A sequence-based selection hyper-heuristic with online learning is used to optimise 12 water distribution networks of varying sizes. The hyper-heuristic results are compared with those produced by five multi-objective evolutionary algorithms. The comparison demonstrates that the hyper-heuristic is a computationally efficient alternative to a multi-objective evolutionary algorithm. An offline learning algorithm is used to enhance the optimisation performance of the hyper-heuristic. The optimisation results of the offline trained hyper-heuristic are analysed statistically, and a new offline learning methodology is proposed. The new methodology is evaluated, and shown to produce an improvement in performance on each of the 12 networks. Finally, it is demonstrated that offline learning can be usefully transferred from small, computationally inexpensive problems, to larger computationally expensive ones, and that the improvement in optimisation performance is statistically significant, with 99% confidence.

Section: An Offline Subsequence Databasementioning

confidence: 99%

Offline Learning with a Selection Hyper-Heuristic: An Application to Water Distribution Network Optimisation

Evolutionary Computation

2021

“…All the algorithms developed in this paper are trained and tested on the Hyper-heuristics Flexible framework (or HyFlex, see Ochoa et al 2012). HyFlex is a set of benchmark problems that has been used in a number of studies (see for example Walker et al 2012;Drake et al 2012;Mısır et al 2013;Drake et al 2015;Kheiri and Keedwell 2015;Dempster and Drake 2016). HyFlex 2 is an implementation of six computationally hard problem domains:…”

Section: Hard Problems and The Hyflex Frameworkmentioning

confidence: 99%

“…When crossover heuristics are available, the choice of crossover mechanism also affects hyper-heuristic performance (see Drake et al 2015). The DBGen crossover mechanism is a simplification of the crossover management scheme employed by AdapHH (see Drake et al 2015) which uses a population of five potential crossover solutions including the current best solution. The DBGen crossover mechanism employs a population of one solution (lines 12-15).…”

Section: The Dbgen Hyper-heuristicmentioning

confidence: 99%

An analysis of heuristic subsequences for offline hyper-heuristic learning

2019

J Heuristics

A selection hyper-heuristic is used to minimise the objective functions of a wellknown set of benchmark problems. The resulting sequences of low level heuristic selections and objective function values are used to generate a database of heuristic selections. The sequences in the database are broken down into subsequences and the mathematical concept of a logarithmic return is used to discriminate between "effective" subsequences, which tend to decrease the objective value, and "disruptive" subsequences, which tend to increase the objective value. These subsequences are then employed in a sequenced based hyper-heuristic and evaluated on an unseen set of benchmark problems. Empirical results demonstrate that the "effective" subsequences perform significantly better than the "disruptive" subsequences across a number of problem domains with 99% confidence. The identification of subsequences of heuristic selections that can be shown to be effective across a number of problems or problem domains could have important implications for the design of future sequence based hyper-heuristics.

“…See for example [9], [10], [11], [12], [5], and [13]. HyFlex contains an implementation of four computationally hard problem domains:…”

Section: Hyflex and The Offline Learning Databasementioning

confidence: 99%

Offline Learning for Selection Hyper-heuristics with Elman Networks

Lecture Notes in Computer Science

2018

Abstract. Offline selection hyper-heuristics are machine learning methods that are trained on heuristic selections to create an algorithm that is tuned for a particular problem domain. In this work, a simple selection hyper-heuristic is executed on a number of computationally hard benchmark optimisation problems, and the resulting sequences of low level heuristic selections and objective function values are used to construct an offline learning database. An Elman network is trained on sequences of heuristic selections chosen from the offline database and the network's ability to learn and generalise from these sequences is evaluated. The networks are trained using a leave-one-out cross validation methodology and the sequences of heuristic selections they produce are tested on benchmark problems drawn from the HyFlex set. The results demonstrate that the Elman network is capable of intra-domain learning and generalisation with 99% confidence and produces better results than the training sequences in many cases. When the network was trained using an interdomain training set, the Elman network did not exhibit generalisation indicating that inter-domain generalisation is a harder problem and that strategies learned on one domain cannot necessarily be transferred to another.