Communications of the ACM
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Randomized algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
A Review of Theoretical and Experimental Results on Schemata in Genetic Programming
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Minimum spanning trees made easier via multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
An adverse interaction between crossover and restricted tree depth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Bloat control operators and diversity in genetic programming: A comparative study
Evolutionary Computation
Operator equalisation and bloat free GP
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
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
Computing Minimum Cuts by Randomized Search Heuristics
Algorithmica - Special Issue: Theory of Evolutionary Computation
Algorithmica - Special Issue: Theory of Evolutionary Computation
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Simple max-min ant systems and the optimization of linear pseudo-boolean functions
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
PAC learning and genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions
IEEE Transactions on Evolutionary Computation
The max problem revisited: the importance of mutation in 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
Parsimony pressure versus multi-objective optimization for variable length representations
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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The computational complexity analysis of genetic programming (GP) has been started recently in [7] by analyzing simple (1+1) GP algorithms for the problems ORDER and MAJORITY. In this paper, we study how taking the complexity as an additional criteria influences the runtime behavior. We consider generalizations of ORDER and MAJORITY and present a computational complexity analysis of (1+1) GP using multi-criteria fitness functions that take into account the original objective and the complexity of a syntax tree as a secondary measure. Furthermore, we study the expected time until simple multi-objective genetic programming algorithms have computed the Pareto front when taking the complexity of a syntax tree as an equally important objective.