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On the behaviour of evolution strategies optimising cigar functions
Dirk V Arnold1, Hans-Georg Beyer
1Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada. dirk@cs.dal.ca
Evolutionary Computation
|September 3, 2010
Summary
This study evaluates multi-recombinative evolution strategies for optimizing cigar functions. Cumulative step length adaptation shows comparable performance to optimal step lengths in stationary states.
Area of Science:
- Optimization algorithms
- Evolutionary computation
Background:
- Cigar functions are challenging optimization problems with specific Hessian eigenvalue properties.
- Evolution strategies are a class of algorithms used for optimization.
Purpose of the Study:
- To analyze the performance of multi-recombinative evolution strategies with cumulative step length adaptation on cigar functions.
- To compare this adaptation method with optimally adapted step lengths.
Main Methods:
- Developing a simplified model of the strategy's behavior.
- Deriving approximate expressions for the stationary state with adapted mutation strength.
Main Results:
- The study provides insights into the stationary state behavior of the strategies.
- Performance of cumulative step length adaptation is evaluated against optimal step lengths.
Conclusions:
- Cumulative step length adaptation offers a viable approach for optimizing cigar functions.
- The findings contribute to understanding the dynamics of evolution strategies in specific optimization landscapes.
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