Abstract
A genetic algorithm (GA) is heuristic search that replicate the process of natural selection. It is inspired by natural evolution techniques such as selection, crossover and mutation strategies. The analytical hierarchy process (AHP) is used to conceptualize complex problems. The recently developed Cohort Intelligence (CI) algorithm models behavior of individuals within the group. The research for recommending an ice cream to a diabetic patient with respect to GA, CI and with AHP is carried out. The set of equations for GA, CI with respect to AHP are proposed. AHP-GA and AHP-CI will not only verify the previous obtained results for AHP but also shows improvement in results to recommend an ice cream to a diabetic patient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gaikwad, S.M., Mulay, P., Joshi, R.R.: Analytical hierarchy process to recommend an ice cream to a diabetic patient based on sugar content in it. Procedia Comput. Sci. 50, 64–72 (2015)
Babu, P.H., Gopi, E.S.: Medical data classifications using genetic algorithm based generalized kernel linear discriminant analysis. Procedia Comput. Sci. 57, 868–875 (2015)
Chen, J., Zhang, C.: Efficient clustering method based on rough set and genetic algorithm. Procedia Eng. 15, 1498–1503 (2011)
Haikal, A., El-Hosseni, M.: Modified cultural-based genetic algorithm for process optimization. Ain Shams Eng. J. 2, 173–182 (2011)
Ji, Z., Li, Z., Ji, Z.: Research on genetic algorithm and data information based on combined framework for nonlinear functions optimization. Procedia Eng. 23, 155–160 (2011)
Khamrui, A., Mandal, J.K.: A genetic algorithm based steganography using discrete cosine transformation (GASDCT). Procedia Technol. 10, 105–111 (2013)
Kiyoumarsi, F.: Mathematics programming based on genetic algorithms education. Procedia Soc. Behav. Sci. 192, 70–76 (2015)
López-EspÃn, J.J., Giménez, D.: Obtaining simultaneous equation models from a set of variables through genetic algorithms. Procedia Comput. Sci. 1, 427–435 (2010)
Sharma, P., Saroj: Discovery of classification rules using distributed genetic algorithm. Procedia Comput. Sci. 46, 276–284 (2015)
Yan, L., Gui, Z., Du, W., Guo, Q.: An improved PageRank method based on genetic algorithm for web search. Procedia Eng. 15, 2983–2987 (2011)
Gaikwad, S.M.: Cluster mapping with the help of new proposed algorithm and MCF algorithm to recommend an ice cream to the diabetic Patient. Int. J. Appl. Eng. Res. 10, 21259–21266 (2015)
Gaikwad, S.M., Joshi, R.R., Mulay, P.: Analytical Network Process (ANP) to recommend an ice cream to a diabetic patient. IJCA 121(12), 49–52 (2015)
Gaikwad, S.M., Joshi, R.R., Mulay, P.: System dynamics modeling for analyzing recovery rate of diabetic patients by mapping sugar content in ice cream and sugar intake for the day. In: Satapathy, S.C., Raju, K.S., Mandal, J.K., Bhateja, V. (eds.) IC3T 2015. AISC, vol. 379, pp. 743–749. Springer, Heidelberg (2016). doi:10.1007/978-81-322-2517-1_71
Gaikwad, S.M., Mulay, P., Joshi, R.R.: Mapping with the help of new proposed algorithm and modified cluster formation algorithm to recommend an ice cream to the diabetic patient based on sugar conatin in it. Int. J. Students Res. Technol. Manage. 3, 410–412 (2015)
Gaikwad, S.M., Joshi, R.R., Mulay, P.: Attribute visualization and cluster mapping with the help of new proposed algorithm and modified cluster formation algorithm to recommend an ice cream to the diabetic patient based on sugar contain in it. Int. J. Appl. Eng. Res. 10, 1–6 (2015)
Gaikwad, S.M.: Cluster mapping with the help of new proposed algorithm and MCF algorithm to recommend an ice cream to the diabetic patient. Int. J. Appl. Eng. Res. 10, 21259–21266 (2015)
Gaikwad, S.M., Joshi, R.R., Mulay, P.: Modified analytical hierarchy process to recommend an ice cream to a diabetic patient, pp. 1–6 (2015)
Kulkarni, A.J., Durugkar, I.P., Kumar, M.: Cohort intelligence: a self supervised learning behavior. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 1396–1400 (2013)
Krishnasamy, G., Kulkarni, A.J., Paramesaran, R.: A hybrid approach for dataclustering based on modified cohort intelligence and K-means. Expert Syst. Appl. 41(13), 6009–6016 (2014)
Kulkarni, A.J., Shabir, H.: Solving 0–1 Knapsack problem using cohort intelligence algorithm. Int. J. Mach. Learn. Cybern. 7, 427–441 (2014). doi:10.1007/s13042-014-0272-y
Kulkarni, A.J.: Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems. Eur. J. Oper. Res. (2015). doi:10.1016/j.ejor.2015.10.008
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Gaikwad, S.M., Joshi, R.R., Kulkarni, A.J. (2016). Cohort Intelligence and Genetic Algorithm Along with AHP to Recommend an Ice Cream to a Diabetic Patient. In: Panigrahi, B., Suganthan, P., Das, S., Satapathy, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2015. Lecture Notes in Computer Science(), vol 9873. Springer, Cham. https://doi.org/10.1007/978-3-319-48959-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-48959-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48958-2
Online ISBN: 978-3-319-48959-9
eBook Packages: Computer ScienceComputer Science (R0)