Diversity as a selection pressure in dynamic environments

Authors:
Lam T. Bui;Jürgen Branke;Hussein A. Abbass
Affiliations:
University of New South Wales @ ADFA, Canberra, Australia;University of Karlsruhe, Karlsruhe, Germany;University of New South Wales @ ADFA, Canberra, Australia
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

Citing 3
Cited 6

Evolutionary Optimization in Dynamic Environments

Evolutionary Optimization in Dynamic Environments
Genetic diversity as an objective in multi-objective evolutionary algorithms

Evolutionary Computation
Searching under multi-evolutionary pressures

EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization

A Hooke-Jeeves Based Memetic Algorithm for Solving Dynamic Optimisation Problems

HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Improving computational mechanics optimum design using helper objectives: an application in frame bar structures

EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
An adaptive optimization technique for dynamic environments

Engineering Applications of Artificial Intelligence
An analysis of multi-chromosome GAs in deceptive problems

Proceedings of the 13th annual conference on Genetic and evolutionary computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study

Evolutionary Computation
Evolving team behaviors with specialization

Genetic Programming and Evolvable Machines

Quantified Score

Hi-index	0.00

Visualization

Abstract

Evolutionary algorithms (EAs) are widely used to deal with optimization problems in dynamic environments (DE) [3]. When using EAs to solve DE problems, we are usually interested in the algorithm's ability to adapt and recover from the changes. One of the main problems facing an evolutionary method when solving DE problems is the loss of genetic diversity.In this paper, we investigate the use of evolutionary multi-objective optimization methods (EMOs) for single-objective DE problems. For that purpose, we introduce an artificial second objective with the aim to maintain useful diversity in the population. Six different artificial objectives are examined and compared.All the results will be compared against a traditional GA and the random immigrants algorithm[4]. NSGA2 is employed as the evolutionary multi-objective technique.