Genotypic differences and migration policies in an island model
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Expectation maximization enhancement with evolutionstrategy for stochastic ontology mapping
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A Formal Approach to Heuristically Test Restorable Systems
ICTAC '09 Proceedings of the 6th International Colloquium on Theoretical Aspects of Computing
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A parallel skeleton for genetic algorithms
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Using a genetic algorithm for the determination of power load profiles
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Proceedings of the 28th Annual ACM Symposium on Applied Computing
An ACO-RFD hybrid method to solve NP-complete problems
Frontiers of Computer Science: Selected Publications from Chinese Universities
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The field of Evolutionary Computation has experienced tremendous growth over the past 20 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to se the relationships between them, assess strengths and weaknesses, and make good choices for new application areas. This tutorial is intended to give an overview of a general EC framework that can help compare and contrast approaches, encourages crossbreeding, and facilitates intelligent design choices. The use of this framework is then illustrated by showing how traditional EAs can be compared and contrasted with it, and how new EAs can be effectively designed using it. Finally, the framework is used to identify some important open issues that need further research.