Experimental Methods for the Analysis of Optimization Algorithms
In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, comp...
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| Other Authors: | , , , |
| Format: | Electronic |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2010.
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| Online Access: | https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-02538-9 |
Table of Contents:
- Introduction
- Concepts and Practice of Algorithm Engineering
- Generating Experimental Data for Computational Testing in Scheduling Problems
- On the Performance Testing of Combinatorial Optimization Algorithms: The Scientific Method
- Algorithm Survival Analysis
- On Applications of Extreme Value Theory in Optimization
- F-Race and Further Enhancements
- Comparing the Performance of Evolutionary Algorithms with Multiple Hypothesis Testing
- Mixed Models for the Analysis of Local Search Components
- Sequential Experiment Designs for Screening and Tuning Parameters of Stochastic Heuristics
- Sequential Parameter Optimization (SPO) and the Role of Tuning in Experimental Analysis
- An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis
- The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison
- Experimental Analysis of Stochastic Local Search Components for Multiobjective Problems
- An Introduction to Inferential Statistics.