Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Optimal Expected-Time Algorithms for Closest Point Problems
ACM Transactions on Mathematical Software (TOMS)
Multidimensional binary search trees used for associative searching
Communications of the ACM
Testing the Results of Static Worst-Case Execution-Time Analysis
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Statistical Analysis of WCET for Scheduling
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Expert Systems with Applications: An International Journal
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
The worst-case execution-time problem—overview of methods and survey of tools
ACM Transactions on Embedded Computing Systems (TECS)
An Empirical Comparison of Exact Nearest Neighbour Algorithms
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Evolutionary software engineering, a review
Applied Soft Computing
| Hi-index | 12.05 |
When computational methods are developed, the efficiency of the novel methods should be compared to the existing ones. This can be done using, e.g., analytical methods and benchmark test patterns. In addition, the comparison of the best and the worst case performance is commonly of interest. In this paper, methodologies of genetic algorithm based software testing are adopted to the comparative computational testing of three varieties of dynamic two-dimensional nearest point algorithms. The extreme performances of the algorithms are searched for by optimizing the shape of two-dimensional Gaussian distributions, from which the test patterns are drawn. In particular, an approach to pairwise comparisons of computational complexities of algorithms is proposed. The test case algorithms can be sorted with respect to their computational complexity by the proposed method.