Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Formal Algorithms + Formal Representations = Search Strategies
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Holder functions and deception of genetic algorithms
IEEE Transactions on Evolutionary Computation
A Framework for Distributed Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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The EASEA language (EAsy Specification of Evolutionary Algorithms) was created in order to allow scientists to concentrate on evolutionary algorithm design rather than implementation. EASEA currently supports two C++ libraries (GALib and EO) and a JAVA library for the DREAM. The aim of this paper is to assess the quality of EASEA-generated code through an extensive test procedure comparing the implementation for EO and GALib of the same test functions.