4 research outputs found
Hyper-heuristic decision tree induction
A hyper-heuristic is any algorithm that searches or operates in the space of
heuristics as opposed to the space of solutions. Hyper-heuristics are
increasingly used in function and combinatorial optimization. Rather than
attempt to solve a problem using a fixed heuristic, a hyper-heuristic
approach attempts to find a combination of heuristics that solve a problem
(and in turn may be directly suitable for a class of problem instances).
Hyper-heuristics have been little explored in data mining. This work presents
novel hyper-heuristic approaches to data mining, by searching a space of
attribute selection criteria for decision tree building algorithm. The search is
conducted by a genetic algorithm. The result of the hyper-heuristic search in
this case is a strategy for selecting attributes while building decision trees.
Most hyper-heuristics work by trying to adapt the heuristic to the state of
the problem being solved. Our hyper-heuristic is no different. It employs a
strategy for adapting the heuristic used to build decision tree nodes
according to some set of features of the training set it is working on. We
introduce, explore and evaluate five different ways in which this problem
state can be represented for a hyper-heuristic that operates within a decisiontree
building algorithm. In each case, the hyper-heuristic is guided by a rule
set that tries to map features of the data set to be split by the decision tree
building algorithm to a heuristic to be used for splitting the same data set.
We also explore and evaluate three different sets of low-level heuristics that
could be employed by such a hyper-heuristic.
This work also makes a distinction between specialist hyper-heuristics and
generalist hyper-heuristics. The main difference between these two hyperheuristcs
is the number of training sets used by the hyper-heuristic genetic
algorithm. Specialist hyper-heuristics are created using a single data set from
a particular domain for evolving the hyper-heurisic rule set. Such algorithms
are expected to outperform standard algorithms on the kind of data set used
by the hyper-heuristic genetic algorithm. Generalist hyper-heuristics are
trained on multiple data sets from different domains and are expected to
deliver a robust and competitive performance over these data sets when
compared to standard algorithms.
We evaluate both approaches for each kind of hyper-heuristic presented in
this thesis. We use both real data sets as well as synthetic data sets. Our
results suggest that none of the hyper-heuristics presented in this work are
suited for specialization – in most cases, the hyper-heuristic’s performance on
the data set it was specialized for was not significantly better than that of
the best performing standard algorithm. On the other hand, the generalist
hyper-heuristics delivered results that were very competitive to the best
standard methods. In some cases we even achieved a significantly better
overall performance than all of the standard methods
An investigation of multi-objective hyper-heuristics for multi-objective optimisation
In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function.
GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems
An investigation of multi-objective hyper-heuristics for multi-objective optimisation
In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function.
GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems