Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
On evolutionary exploration and exploitation
Fundamenta Informaticae
Advanced Population Diversity Measures in Genetic Programming
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Metric for Genetic Programs and Fitness Sharing
Proceedings of the European Conference on Genetic Programming
Promoting phenotypic diversity in genetic programming
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Evolving art using multiple aesthetic measures
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Phenotypic diversity in initial genetic programming populations
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Comparing aesthetic measures for evolutionary art
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Diversity in genetic programming: an analysis of measures and correlation with fitness
IEEE Transactions on Evolutionary Computation
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Global contrast factor - a new approach to image contrast
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Benford's law for natural and synthetic images
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
| Hi-index | 0.00 |
Evolutionary art is inherently more concerned with exploration than with exploitation, because users are typically more interested in evolving a collection of diverse images than converging to a single ‘optimal' image. However, maintaining diversity is a difficult task. In this paper we investigate various techniques to promote population diversity in evolutionary art. We introduce customised mutation and crossover operators that perform a local search to diversify individuals and evaluate the effect of these operators on population diversity. We also investigate alternatives for the fitness crowding operator in NSGA-II; we use a genotype and a phenotype distance function to calculate the crowding distance and investigate their effect on population diversity.