By Hiroyuki Yoshida, Ashlesha Jain, Ajita Ichalkaranje, Nikhil Ichalkaranje
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Additional resources for Advanced Computational Intelligence Paradigms in Healthcare-2
7%). 5 also shows (lower part) the goodness of the best solution to which the corresponding alleles belong. The goodness 42 B. Ga´ al et al. Fig. 5. 000 runs. e. the best ﬁtness value of all solutions the allele was part of) with the weighted best ﬁtness values of its 8 neighbors. 2j) · (f [i − j] + f [i + j])] g[i] = f [i] + j=1 where f [i] is the ﬁtness of the ith allele. The trend curve (lower part) shows that solutions containing frequent alleles have generally better goodness. 3 Application of Artiﬁcial Intelligence for Weekly Dietary Menu Planning 43 The results show, that alleles appearing in good solutions are used more often by the algorithm and the frequency of usage is about inverse proportional to the ﬁtness of the best solution generated by using the particular allele.
S. S. G. S. Wigton Selection of Predictor Variables for Pneumonia Using Neural Networks and Genetic Algorithms, Methods Inf Med 2005; 44: 89–97 48 B. Ga´ al et al. 19. : A comprehensive survey of evolutionary-based multiobjective optimization techniques, Int. J. Knowledge Inform. Syst 1, 269–309, 1999 20. Multi-level Multi-objective Genetic Algorithm Using Entropy to Preserve Diversity, EMO 2003, LNCS 2632, pp. 148–161, 2003 21. T. edu/ga/ [Veriﬁed June 2006] 4 Evaluation of Healthcare IT Applications: The User Acceptance Perspective Kai Zheng1 , Rema Padman2 , Michael P.
3. Runtime of MenuGene in various algorithmic setups. Each dot represents the result of a test run 40 B. Ga´ al et al. Fig. 4. 2. 2 iterations on average (14 iterations in the worst case). As Fig. 4 shows, there is hardly any improvement in the quality of the solution after 250 iterations, so a nearly optimal plan can be found in this time. 2 Crossover and Mutation We analyzed the eﬀect of the crossover and mutation probabilities on the ﬁtness. We used the same randomly generated initial populations for the tests, and averaged the results of ten runs in each conﬁguration.
Advanced Computational Intelligence Paradigms in Healthcare-2 by Hiroyuki Yoshida, Ashlesha Jain, Ajita Ichalkaranje, Nikhil Ichalkaranje
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