On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
EuroGP '98 Proceedings of the First European Workshop 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
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Algorithmica - Special Issue: Theory of Evolutionary Computation
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
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
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
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We consolidate the existing computational complexity analysis of genetic programming (GP) by bringing together sound theoretical proofs and empirical analysis. In particular, we address computational complexity issues arising when coupling algorithms using variable length representation, such as GP itself, with different bloat-control techniques. In order to accomplish this, we first introduce several novel upper bounds for two single- and multi-objective GP algorithms on the generalised Weighted ORDER and MAJORITY problems. To obtain these, we employ well-established computational complexity analysis techniques such as fitness-based partitions, and for the first time, additive and multiplicative drift. The bounds we identify depend on two measures, the maximum tree size and the maximum population size, that arise during the optimization run and that have a key relevance in determining the runtime of the studied GP algorithms. In order to understand the impact of these measures on a typical run, we study their magnitude experimentally, and we discuss the obtained findings.