Applying soft computing methods in business analytics using hybrid genetic algorithms (Record no. 22706)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250424105724.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250424b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25971 |
| Author | Senthil Pandian, P. |
| 245 ## - TITLE STATEMENT | |
| Title | Applying soft computing methods in business analytics using hybrid genetic algorithms |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.15(2), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3539-3544p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | In the era of big data and advanced analytics, the application of soft<br/>computing techniques has emerged as a powerful tool in solving<br/>complex business problems. This paper presents the use of hybrid<br/>genetic algorithms (HGAs) in business analytics to address challenges<br/>related to optimization, prediction, and decision-making processes.<br/>Traditional algorithms often struggle with large, nonlinear, and<br/>dynamic datasets typical of business environments. The incorporation<br/>of soft computing techniques such as genetic algorithms (GAs) and<br/>their hybridization with other methods like fuzzy logic and neural<br/>networks can help overcome these limitations. The problem addressed<br/>in this research is optimizing decision-making in marketing strategies,<br/>focusing on maximizing return on investment (ROI). Standard<br/>methods face difficulties in navigating through vast datasets and<br/>discovering optimal solutions. The hybrid genetic algorithm proposed<br/>in this study combines the exploration strength of GAs with the<br/>exploitative precision of local search techniques. The model was tested<br/>using a real-world dataset of marketing expenditures and revenues<br/>from a retail company. The HGA achieved an ROI improvement of<br/>25%, significantly outperforming standard GAs and traditional<br/>optimization methods, which yielded only a 12% improvement. The<br/>flexibility and efficiency of this approach make it ideal for various<br/>business applications, including supply chain optimization, customer<br/>segmentation, and product pricing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25972 |
| Co-Author | Anakal, Sudhir |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_9_3539_3544.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 24/04/2025 | 2025-0655 | 24/04/2025 | 24/04/2025 | Articles Abstract Database |