Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Minimum spanning trees made easier via multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Approximating covering problems by randomized search heuristics using multi-objective models*
Evolutionary Computation
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Non-uniform mutation rates for problems with unknown solution lengths
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions
IEEE Transactions on Evolutionary Computation
Computational complexity analysis of multi-objective genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
The max problem revisited: the importance of mutation in genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Single- and multi-objective genetic programming: new bounds for weighted order and majority
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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We contribute to the theoretical understanding of variable length evolutionary algorithms. Such algorithms are very flexible but can encounter the bloat problem which means solutions grow during the optimization run without providing additional benefit. We explore two common mechanisms for dealing with this problem from a theoretical point of view and point out the differences of a parsimony and a multi-objective approach in a rigorous way. As an example to point out the differences, we consider different measures of sortedness for the classical sorting problem which has already been studied in the computational complexity analysis of evolutionary algorithms with fixed length representations.