Information and Software Technologies: 18th International by Algirdas Laukaitis (auth.), Tomas Skersys, Rimantas

By Algirdas Laukaitis (auth.), Tomas Skersys, Rimantas Butleris, Rita Butkiene (eds.)

This ebook constitutes the refereed court cases of the 18th overseas convention on info and software program applied sciences, ICIST 2012, held in Kaunas, Lithuania, in September 2012. The forty revised complete papers offered have been rigorously reviewed and chosen from eighty one submissions. The papers are prepared in topical sections on man made intelligence and data engineering, enterprise method modelling, research and layout, formal research and layout tools, info and software program platforms engineering, details know-how functions and desktop networks, info expertise in educating and studying, ontology, conceptual modelling and databases, requisites engineering and company rules.

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13, 124–137 (2006) 13. : Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989) 14. : Messy genetic algorithms: Motivation, analysis and first results. Compl. Syst. 3, 493–530 (1989) 15. : Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975) 16. : Microgenetic algorithms as generalized hill-climbing operators for GA optimization. IEEE Trans. Evol. Comput. 5, 204–217 (2001) 17. : Assignment problems and the location of economic activities.

Then, the mutation procedure takes place every τ iterations and the 1 mutation frequency within the ITS procedure is thus determined by the ratio . τ On the other hand, there are λ calls to the ITS procedure per one generation, which results in λ(Q − 1) mutations per generation. The other regulatory parameter is the mutation strength, ρ, which is formally defined as: ρ = HD(π , π~ ) = {i | π (i ) ≠ π~ (i )} , (2) where HD(π , π~ ) denotes the Hamming distance between permutations, and π, π~ are the permutations (chromosomes) before and after mutation, respectively.

This concept is not new and it is in connection with what is known in the literature as "microgenetic algorithms" [16]. • Some variations of the extensified population initialization, at the cost of the decreased number of generations, are possible. The following are two proposed strategies, which have some relationship to so-called compounded GAs [9]. ─ In the first, one-stage improvement strategy, the random population, P, of size α × PS is initially generated, which is then improved and truncated by removing (α − 1) × PS worst members.

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