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Experimental evaluation of heuristic optimization algorithms: A tutorial. (English) Zbl 0972.68634
Summary: Heuristic optimization algorithms seek good feasible solutions to optimization problems in circurnstances where the complexities of the problem or the limited time available for solution do not allow exact solution. Although worst case and probabilistic analysis of algorithms have produced insight on some classic models, most of the heuristics developed for large optimization problem must be evaluated empirically – by applying procedures to a collection of specific instances and comparing the observed solution quality and computational burden.
This paper focuses on the methodological issues that must be confronted by researchers undertaking such experimental evaluations of heuristics, including experimental design, sources of test instances, measures of algorithmic performance, analysis of results, and presentation in papers and talks. The questions are difficult, and there are no clear right answers. We seek only to highlight the main issues, present alternative ways of addressing them under different circumstances, and caution about pitfalls to avoid.

MSC:
68U99 Computing methodologies and applications
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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