Multi-Objective Optimization: Evolutionary to Hybrid Framework
β Scribed by Jyotsna K. Mandal, Somnath Mukhopadhyay, Paramartha Dutta
- Publisher
- Springer Singapore
- Year
- 2018
- Tongue
- English
- Leaves
- 326
- Edition
- 1st ed.
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This book brings together the latest findings on efficient solutions of multi/many-objective optimization problems from the leading researchers in the field. The focus is on solving real-world optimization problems using strategies ranging from evolutionary to hybrid frameworks, and involving various computation platforms.
The topics covered include solution frameworks using evolutionary to hybrid models in application areas like Analytics, Cancer Research, Traffic Management, Networks and Communications, E-Governance, Quantum Technology, Image Processing, etc. As such, the book offers a valuable resource for all postgraduate students and researchers interested in exploring solution frameworks for multi/many-objective optimization problems.β¦ Table of Contents
Front Matter ....Pages i-xvi
Non-dominated Sorting Based Multi/Many-Objective Optimization: Two Decades of Research and Application (Haitham Seada, Kalyanmoy Deb)....Pages 1-24
Mean-Entropy Model of Uncertain Portfolio Selection Problem (Saibal Majumder, Samarjit Kar, Tandra Pal)....Pages 25-54
Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data (Anirban Mukhopadhyay)....Pages 55-78
Interval-Valued Goal Programming Method to Solve Patrol Manpower Planning Problem for Road Traffic Management Using Genetic Algorithm (Bijay Baran Pal)....Pages 79-113
Multi-objective Optimization to Improve Robustness in Networks (R. Chulaka Gunasekara, Chilukuri K. Mohan, Kishan Mehrotra)....Pages 115-139
On Joint Maximization in Energy and Spectral Efficiency in Cooperative Cognitive Radio Networks (Santi P. Maity, Anal Paul)....Pages 141-157
Multi-Objective Optimization Approaches in Biological Learning System on Microarray Data (Saurav Mallik, Tapas Bhadra, Soumita Seth, Sanghamitra Bandyopadhyay, Jianjiao Chen)....Pages 159-180
Application of Multiobjective Optimization Techniques in Biomedical Image SegmentationβA Study (Shouvik Chakraborty, Kalyani Mali)....Pages 181-194
Feature Selection Using Multi-Objective Optimization Technique for Supervised Cancer Classification (P. Agarwalla, S. Mukhopadhyay)....Pages 195-213
Extended Nondominated Sorting Genetic Algorithm (ENSGA-II) for Multi-Objective Optimization Problem in Interval Environment (Asoke Kumar Bhunia, Amiya Biswas, Ali Akbar Shaikh)....Pages 215-241
A Comparative Study on Different Versions of Multi-Objective Genetic Algorithm for Simultaneous Gene Selection and Sample Categorization (Asit Kumar Das, Sunanda Das)....Pages 243-267
A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation (Niladri Sekhar Datta, Himadri Sekhar Dutta, Koushik Majumder, Sumana Chatterjee, Najir Abdul Wasim)....Pages 269-278
Bi-objective Genetic Algorithm with Rough Set Theory for Important Gene Selection in Disease Diagnosis (Asit Kumar Das, Soumen Kumar Pati)....Pages 279-298
Multi-Objective Optimization and Cluster-Wise Regression Analysis to Establish InputβOutput Relationships of a Process (Amit Kumar Das, Debasish Das, Dilip Kumar Pratihar)....Pages 299-318
β¦ Subjects
Computer Science; Mathematics of Computing; Optimization; Computational Intelligence
π SIMILAR VOLUMES
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has bee
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has bee
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