By William E. Hart, Natalio Krasnogor, J.E. Smith
Memetic algorithms are evolutionary algorithms that observe a neighborhood seek approach to refine options to difficult difficulties. Memetic algorithms are the topic of extreme medical study and feature been effectively utilized to a large number of real-world difficulties starting from the development of optimum collage examination timetables, to the prediction of protein constructions and the optimum layout of space-craft trajectories. This monograph provides a wealthy cutting-edge gallery of works on memetic algorithms. fresh Advances in Memetic Algorithms is the 1st booklet that specializes in this know-how because the principal topical subject. This booklet offers a coherent, built-in view on either reliable perform examples and new developments together with a concise and self-contained creation to memetic algorithms. it's a important learn for postgraduate scholars and researchers drawn to contemporary advances in seek and optimization applied sciences in response to memetic algorithms, yet can be used as supplement to undergraduate textbooks on man made intelligence.
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Additional info for Recent Advances in Memetic Algorithms
2000) Solving Large Binary Quadratic Programming Problems by Effective Genetic Local Search Algorithm. In: Proceedings of the 2000 Genetic and Evolutionary Computation Conference 643650 11. , Narihisa, H. (2001) A Variant k-opt Local Search Heuristic for Binary Quadratic Programming. Trans. IEICE (A) J84-A : 430-435 (in Japanese) 12. , Narihisa, H. (2001) On Fundamental Design of Parthenogenetic Algorithm for the Binary Quadratic Programming Problem. In: Proceedings of the 2001 Congress on Evolutionary Computation 356-363 13.
The results demonstrate that the k-flip local search based MA is more effective than a MA based on 2-flip local search in terms of final solutions, particularly for large-scale problem instances. The paper is organized as follows. In the next section, we show the k-flip local search incorporated in the memetic algorithm for the MDP. In section 3, a flow of the memetic algorithm is given, and each operation in the algorithm is described. In section 4, we report experimental results for the memetic algorithms tested on our new problem instances and on several benchmarks derived from well-known BQP7sones in which the d i j coefficients are not restricted to non-negative values.
Smith 20. : Artificial intelligence through a simulation of evolution. : Biophysics and Cybernetic Systems. Spartan, Washington DC (1965) 131-156 21. : Artificial Intelligence through Simulated Evolution. Wiley, Chichester, UK (1966) 22. : An Analysis of the Behaviour of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan (1975) 23. : Genetic algorithms and the optimal allocation of trials. SIAM J. of Computing 2 (1973) 88-105 24. : Adaption in Natural and Artificial Systems.
Recent Advances in Memetic Algorithms by William E. Hart, Natalio Krasnogor, J.E. Smith