In this paper, we present new results on the automated generalization of performance-related heuristics learned for knowledge-lean applications. By rst applying genetics-based learning to learn new heuristics for some small subsets of test cases in a problem space, we study methods to generalize these heuristics to unlearned subdomains of test cases. Our method uses a new statistical metric cal...

The work presented in this paper could be thought of as a case based hyper-heuristic approach for examination timetabling problems. A hyper-heuristic can be taken to be an automated approach to choose heuristics. Heuristics and meta-heuristics are employed in this capacity in [1] and [2]. In this paper the case-based paradigm is explored as a heuristic selector for examination timetabling probl...

Hyper-heuristics (HHs) stand as a relatively recent approach to solving optimization problems. There are different kinds of HHs. One them deals with how low-level heuristics must be combined deliver an improved solution set problem instances. Literature commonly refers selection hyper-heuristics</i...

Many heuristics for cost-optimal planning are based on linear programming. We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program. Within the framework, constraints from different heuristics can be combined in one heuristic estimate which dominates the maximum of the component heuristics. Different heuristics of the fra...

With respect to questions of fact, people use heuristics – mental short-cuts, or rules of thumb, that generally work well, but that also lead to systematic errors. People use moral heuristics too – moral short-cuts, or rules of thumb, that lead to mistaken and even absurd moral judgments. These judgments are highly relevant not only to morality, but to law and politics as well. Examples are giv...

Adaptive heuristics refer to simple behavioral rules that are directed toward payoff improvement but may be less than fully rational. The number and variety of such rules is virtually unlimited; here we shall survey several prominent examples drawn from psychology, computer science, statistics, and game theory. Of particular interest are the informational inputs required by different learning h...