More on computational effort statistics for genetic programming

Authors:
Jens Niehaus;Wolfgang Banzhaf
Affiliations:
Computer Science Department, University of Dortmund, Dortmund, Germany;Computer Science Department, University of Dortmund, Dortmund, Germany
Venue:
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Year:
2003

Citing 4
Cited 9

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications

Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Is The Perfect The Enemy Of The Good?

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An Analysis of Koza's Computational Effort Statistic for Genetic Programming

EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming

Reducing the Number of Fitness Evaluations in Graph Genetic Programming Using a Canonical Graph Indexed Database

Evolutionary Computation
Parallel evolution using multi-chromosome cartesian genetic programming

Genetic Programming and Evolvable Machines
Genetic programming that ensures programs are original

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Frankenstein's PSO: a composite particle swarm optimization algorithm

IEEE Transactions on Evolutionary Computation
Confidence intervals for computational effort comparisons

EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Statistical distribution of generation-to-success in GP: application to model accumulated success probability

EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Genetic programming needs better benchmarks

Proceedings of the 14th annual conference on Genetic and evolutionary computation
Achieving COSMOS: a metric for determining when to give up and when to reach for the stars

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Better GP benchmarks: community survey results and proposals

Genetic Programming and Evolvable Machines

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this contribution we take a look at the computational effort statistics as described by KOZA. We transfer the notion from generational genetic programming to tournament-selection (steady-state) GP and show why, in both cases, the measured value of the effort often differs from its theoretical counterpart. It is discussed how systematic estimation errors are introduced by a low number of experiments. Two reasons examined are the number of unsuccessful experiments and the variation in the number of fitness evaluations necessary to find a solution among the successful experiments.