A fitness guided mutation operator for improved performance of MOEAs

Author

Metaxiotis, Kostas ; Liagkouras, K.

Author_Institution

Dept. of Inf., Univ. of Piraeus, Piraeus, Greece

fYear

2013

fDate

8-11 Dec. 2013

Firstpage

751

Lastpage

754

Abstract

In this paper we present a new fitness guided version of the classical polynomial mutation operator. The experimental results show that the proposed fitness guided polynomial mutation (FGPLM) operator outperforms the classical polynomial mutation operator when applied in Non-dominated Sorting Genetic Algorithm II (NSGAII) in a number of performance measures that evaluate the proximity of the solutions to the Pareto front.

Keywords

Pareto optimisation; genetic algorithms; polynomials; FGPLM; MOEAs; NSGAII; Pareto front; fitness guided polynomial mutation operator; multiobjective optimization evolutionary algorithms; nondominated sorting genetic algorithm II; performance measures; proximity evaluation; Aggregates; Approximation methods; Evolutionary computation; Genetic algorithms; Linear programming; Optimization; Polynomials; Multi-objective optimization; evolutionary algorithms; mutation;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits, and Systems (ICECS), 2013 IEEE 20th International Conference on

Conference_Location

Abu Dhabi

Type

conf

DOI

10.1109/ICECS.2013.6815523

Filename

6815523

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2105942