Use this URL to cite or link to this record in EThOS:
Title: Combination of Evolutionary Algorithms with Decomposition Techniques for Multiobjective Optimization
Author: Li, Hui
ISNI:       0000 0001 3609 3278
Awarding Body: University of Essex
Current Institution: The University of Essex pre-October 2008
Date of Award: 2008
Availability of Full Text:
Full text unavailable from EThOS.
Please contact the current institution’s library for further details.
Multiobjective optimization problems (MOPs) arise in many real-life applications. In recent years, multiobjective evolutionary algorithms (MOEAs) have attracted a growing attention since they are able to find multiple compromise solutions (known as Pareto-optimal solutions) in a single run. Decomposition (i.e., the use of multiple aggregation functions) is one of the basic strategies for fitness assignment in MOEAs. However, the advantages of decomposition techniques have not been fully utilized in Ivl0EAs. This thesis mainly studies the combination of evolutionary algorithms with decomposition techniques for multiobjective optimization.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available