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Nature inspired algorithms have become popular for discovering classification rules due to their ability to effectively handle large and complex search spaces. However, nature inspired algorithms have to compute the fitness of individuals (candidate classification rules) over successive generations repeatedly. Each fitness computation requires scanning the training data. Since the database scan is computationally expensive operation, the execution time for nature inspired algorithms for discovering classification rules grows unreasonably faster for bulky datasets. This paper proposes a novel fitness computation framework for nature inspire algorithms by using a list structure. The indices of instances, covered by every possible attribute-value pair with respect to each class in the training data, are stored in the suggested list structure. The list is prepared only once in advance and stores all information to compute the fitness of any rule that may come up in the life time of the nature inspired algorithm. The existence of the pre-maintained list eliminates the need of scanning training data again and again for fitness computation. We have conducted experiments on 12 datasets from UCI machine learning repository. The results show that the suggested fitness computational framework brings significant speed gain without compromising predictive accuracy. Although, a Genetic Algorithm is used for classification rule discovery as the nature inspired algorithm in this paper, the fitness computation framework is general and can be used with any other nature inspired algorithm. [ABSTRACT FROM AUTHOR]

Background: Severe acute malnutrition (SAM) is a salient health problem in India. Federation of Indian Chamber of Commerce and Industry (FICCI) Research and Analysis Centre, New Delhi, prepared nutreal equivalent to ready-to-use therapeutic food by World Health Organization (WHO) for the management of SAM and defined food like homemade diet.Objective: To compare acceptability and efficacy of nutreal over defined food for the management of SAM.Methods: One hundred twelve children aged less than 5 years with SAM were enrolled as per the standard of WHO. Children were randomized into 2 groups to receive nutreal (n = 56) and defined food (n = 56) in unlimited amounts for 42 consecutive days and extended by 2 weeks as per demand. Calorie and protein intake, weight, and mid-upper arm circumference (MUAC) were recorded daily.Results: Age range was 8 to 45 months. Ninety-three percent of children eagerly accepted nutreal but 7% does not. Whereas in the defined food group, 68% accepted eagerly, 30% did not accept eagerly, and 1.8% accepted poorly ( P = .004). At enrollment, mean weight in the nutreal group was 6.44 ± 1.60 kg and in the defined food group was 8.69 ± 1.76 kg, with MUAC in the nutreal group being 11.12 ± 0.47 cm and in the defined food group being 11.54 ± 0.34 cm. Mean weight in the nutreal and defined food groups at eighth week of intervention was 7.97 ± 1.8 kg and 9.71 ± 1.8 kg ( P < .001), respectively. Mid-upper arm circumference at eighth week was 12.10 ± 0.29 cm in the nutreal group and 12.49 ± 0.50 cm in the defined group ( P < .001).Conclusion: Acceptability, mean weight gain, and MUAC in the nutreal group are greater than the defined food. [ABSTRACT FROM AUTHOR]