Selection hyper-heuristic methodologies explore the space of heuristics which in turn explore the space of candidate solutions for solving hard computational problems. This study investigates the performance of approaches based on a framework that hybridizes selection hyper-heuristics and population based incremental learning (PBIL), mixing offline and online learning mechanisms for solving dyn...

Graph vertex coloring is one of the most studied NP-hard combinatorial optimization problems. Given the hardness of the problem, various heuristic algorithms have been proposed for practical graph coloring, based on local search, population-based approaches and hybrid methods. The research in graph coloring heuristics is very active and improved results have been obtained recently, notably for ...

This paper describes approaches to the vocabulary normalization and identity resolution problems arising during the use of the LOD datasets to populate the content of scholarly knowledge bases. We have proposed new heuristics, using additional information extracted from full text sources of data. The first heuristics uses the full record track of a person and the second one uses self-citation n...

We explore how recommendation techniques can be adapted and applied to big data science to predict data storage locations to users. Specifically, we present a collection of heuristics that use features specific to big data science. We combine these heuristics into a single recommendation engine using a deep recurrent neural network. We show, via analysis of historical Globus operations, that ou...

Symbolic PDBs and Merge-and-Shrink (M&S) are two approaches to derive admissible heuristics for optimal planning. We present a combination of these techniques, Symbolic Merge-and-Shrink (SM&S), which uses M&S abstractions as a relaxation criterion for a symbolic backward search. Empirical evaluation shows that SM&S has the strengths of both techniques deriving heuristics at least as good as the...

Generally speaking, the problem of drawing a graph G is set as a combinatorial optimization problem: producing a layout L(G) of G on a given support according to a drawing convention (e.g. layered drawing) that optimizes some measurable aesthetics (e.g. arc-crossing). Numerous criteria lead to NP-complete problems, and aesthetics often conflict with each other. Hence, various heuristics from cl...

Satisfiability testing (SAT) is a very active area of research today, with numerous real-world applications. We describe CLASS2.0, a genetic programming system for semi-automatically designing SAT local search heuristics. An empirical comparison shows that that the heuristics generated by our GP system outperform the state of the art human-designed local search algorithms, as well as previously...

Neighborhood search heuristics like local search and its variants are some of the most popular approaches to solve discrete optimization problems of moderate to large size. Apart from tabu search, most of these heuristics are memoryless. In this paper we introduce a new neighborhood search heuristic that makes effective use of memory structures in a way that is different from tabu search. We re...

Transport projects are regularly subjected to cost misperformance. The contingency set aside cover any increases in due risk and uncertainty issues is often insufficient. We review approaches that have been used estimate a contingency. show some such as reference class forecasting, which underpins the planning fallacy theory, take biased view formulate Indeed, there perception risks uncertainti...

When implementing a tutoring system that attempts a deep understanding of students’ natural language explanations, there are three basic approaches to choose between; symbolic, in which sentence strings are parsed using a lexicon and grammar; statistical, in which a corpus is used to train a text classifier; and hybrid, in which rich, symbolically produced features supplement statistical traini...