Formal Algorithms + Formal Representations = Search Strategies
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Evolutionary Signal Enhancement Based on Hölder Regularity Analysis
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
A Framework for Distributed Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A GP Artificial Ant for Image Processing: Preliminary Experiments with EASEA
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
Efficient Parallel Implementation of Evolutionary Algorithms on GPGPU Cards
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Speedups between ×70 and ×120 for a generic local search (memetic) algorithm on a single GPGPU chip
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
DEAP: a python framework for evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
INPUT: the intelligent parameter utilization tool
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Visual analysis of population scatterplots
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Using a genetic algorithm for the determination of power load profiles
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Evolutionary algorithms are not straightforward to implement and the lack of any specialised language forces users to reinvent the wheel every time they want to write a new program. Over the last years, evolutionary libraries have appeared, trying to reduce the amount of work involved in writing such algorithms from scratch, by offering standard engines, strategies and tools. Unfortunately, most of these libraries are quite complex to use, and imply a deep knowledge of object programming and C++. To further reduce the amount of work needed to implement a new algorithm, without however throwing down the drain all the man-years already spent in the development of such libraries, we have designed EASEA (acronym for EAsy Specification of Evolutionciry Algorithms): a new high-level language dedicated to the specification of evolutionary algorithms. EASEA compiles .ez files into source files in a target language, containing function calls to a chosen existing library. The resulting source file is in turn compiled and linked with the library to produce an executable file implementing the evolutionary algorithm specified in the original .ez file. EASEA vO.4 is available at: http://www-rocq.inria.fr/EVO-Lab/.