Characterising fitness landscapes with fitness-probability cloud and its applications to algorithm configuration
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Summary
This thesis for the first time bridges the gap between fitness landscape analysis and algorithm configuration, i.e., finding the best suited configuration of a given algorithm for solving a particular problem instance.
- Type
- dissertation
- Published
- 2014-07-01
- Cited by
- 0
- References
- 138
- Access
- Open access
- OpenAlex
- https://openalex.org/W125687371
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5699955
Keywords
Fitness landscape, Fitness approximation, Computer science, Heuristic, Algorithm
References
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- Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
- Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms
- EWLS: A New Local Search for Minimum Vertex Cover
- A Racing Algorithm for Configuring Metaheuristics
- Fitness Distance Correlation Analysis: An Instructive Counterexample
- Conformance testing methodologies and architectures for OSI protocols
- Lectures on Monte Carlo Methods
- What Have You Done for Me Lately? Adapting Operator Probabilities in a Steady-State Genetic Algorithm
- COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-Up Learning
- Stochastic Local Search: Foundations & Applications
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