Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Shall We Repair? Genetic AlgorithmsCombinatorial Optimizationand Feasibility Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
GLEAM - A System for Simulated `Intuitive Learning'
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Lamarckian Evolution, The Baldwin Effect and Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Adding learning to the cellular development of neural networks: Evolution and the baldwin effect
Evolutionary Computation
Resource brokering in grid environments using evolutionary algorithms
PDCN'06 Proceedings of the 24th IASTED international conference on Parallel and distributed computing and networks
A cost-benefit-based adaptation scheme for multimeme algorithms
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Towards an adaptive multimeme algorithm for parameter optimisation suiting the engineers' needs
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Optimised scheduling of grid resources using hybrid evolutionary algorithms
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Solving scheduling problems in grid resource management using an evolutionary algorithm
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part II
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Most successful applications of Evolutionary Algorithms to real world problems employ some sort of hybridization, thus speeding up the optimization process but turning the general applicable Evolutionary Algorithm into a problem-specific tool. This paper proposes to combine Evolutionary Algorithms and generally applicable local searchers to get the best of both approaches: A fast, but robust tool for global optimization. The approach consists of four different kinds of hybridization and combinations thereof, which are tested and compared using five commonly used benchmark functions and three real world applications. The results show the superiority of two hybridization types, with which reductions in the number evaluations of up to a factor of 100 could be achieved.