We present GP-HH, a framework for evolving local-search 3-SAT heuristics based on GP. The aim is to obtain " disposable " heuristics which are evolved and used for a specific subset of instances of a problem. We test the heuristics evolved by GP-HH against well-known local-search heuristics on a variety of benchmark SAT problems. Results are very… (More)
This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a good solution… (More)
We present, GP-HH, a framework for evolving local search 3-SAT heuristics based on GP. Evolved heuristics are compared against well-known SAT solvers with very encouraging results.
Ensemble learning is a machine learning approach that utilises a number of classifiers to contribute via voting to identifying the class label for any unlabelled instances. Random Forests RF is an ensemble classification approach that has proved its high accuracy and superiority. However, most of the commonly used selection methods are static. Motivated by… (More)
This paper presents Inc*, a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. Genetic programming is used to discover new strategies for the Inc* algorithm. We experimentally compare performance of local heuristics for SAT with and… (More)
Class imbalance is a problem that commonly affects 'real world' classification datasets, and has been shown to hinder the performance of classifiers. A dataset suffers from class imbalance when the number of instances belonging to one class outnumbers the number of instance belonging to another class. Two ways of dealing with class imbalance are modifying… (More)
—Random Forest RF is an ensemble learning approach that utilises a number of classifiers to contribute though voting to predicting the class label of any unlabelled instances. Parameters such as the size of the forest N and the number of features used at each split M , has significant impact on the performance of the RF especially on instances with very… (More)
Ensemble learning is a well established machine learning approach that utilises a number of classifiers to aggregate the decision about determining the class label. In its basic form this aggregation is achieved via majority voting. A generic approach, termed EV-Ensemble, for evolving a new ensemble from an existing one is proposed in this paper. This… (More)