Paper Info
Title
Diversity study of multi-objective genetic algorithm based on Shannon entropy.
Abstract
Multi-objective optimization inspired on genetic algorithms are population based search methods. The population elements, chromosomes, evolve using inheritance, mutation, selection and crossover mechanisms. The aim of these algorithms is to obtain a representative non-dominated Pareto front from a given problem. Several approaches to study the convergence and performance of algorithm variants have been proposed, particularly by accessing the final population.In this work, a novel approach by analyzing multi-objective algorithm dynamics during the algorithm execution is considered. The results indicate that Shannon entropy can be used as an algorithm indicator of diversity and convergence.
Year
DOI
Venue
2014
10.1109/NaBIC.2014.6921898
NaBIC
Keywords
Field
DocType
sociology,statistics,indexes,optimization,genetics
Population,Mathematical optimization,Crossover,Computer science,Meta-optimization,Multi-objective optimization,Artificial intelligence,Cultural algorithm,Entropy (information theory),Population-based incremental learning,Genetic algorithm,Machine learning
Conference
ISSN
Citations 
PageRank 
2164-7364
0
0.34
References 
Authors
19
3
Name
Order
Citations
PageRank
E. J. Solteiro Pires18113.95
J. A. Tenreiro Machado250785.77
Paulo B. de Moura Oliveira3309.66