Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
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It is general knowledge that hybrid approaches can improve the performance of search heurististics. The first phase, exploration, should detect regions of good solutions, whereas the second phase, exploitation, shall tune these solutions locally. Therefore a combination (hybridization) of global and local optimization techniques is recommended. Although plausible at the first sight, it remains unclear how to implement the hybridization, e.g., to distribute the resources, i.e., number of function evaluations or CPU time, to the global and local search optimization algorithm. This budget allocation becomes important if the available resources are very limited. We present an approach to analyze hybridization in this case. An evolution strategy and a quasi-Newton method are combined and tested on standard test functions.