A packing problem with applications to lettering of maps
SCG '91 Proceedings of the seventh annual symposium on Computational geometry
An empirical study of algorithms for point-feature label placement
ACM Transactions on Graphics (TOG)
Search, polynomial complexity, and the fast messy genetic algorithm
Search, polynomial complexity, and the fast messy genetic algorithm
The dynamical systems model of the simple genetic algorithm
Theoretical aspects of evolutionary computing
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Using Genetic Algorithms for Solving Hard Problems in GIS
Geoinformatica
Proceedings of the 5th International Conference on Genetic Algorithms
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Convergence Models of Genetic Algorithm Selection Schemes
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Statistical Mechanical Formulation of the Dynamics of Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Schemata evolution and building blocks
Evolutionary Computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
Scalability of selectorecombinative genetic algorithms for problems with tight linkage
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Building a GA from design principles for learning Bayesian networks
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Symbiosis, complexification and simplicity under GP
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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In this paper, we study two recent theoretical models—a population-sizing model and a convergence model—and examine their assumptions to gain insights into the conditions under which selecto-recombinative GAs work well. We use these insights to formulate several design rules to develop competent GAs for practical problems. To test the usefulness of the design rules, we consider as a case study the map-labeling problem, an NP-hard problem from cartography. We compare the predictions of the theoretical models with the actual performance of the GA for the map-labeling problem. Experiments show that the predictions match the observed scale-up behavior of the GA, thereby strengthening our claim that the design rules can guide the design of competent selecto-recombinative GAs for realistic problems.