Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Proceedings of the 3rd International Conference on Genetic Algorithms
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Iterative Refinement Of Computational Circuits Using Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Towards identifying populations that increase the likelihood of success in genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Resource-limited genetic programming: the dynamic approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
Characterizing fault tolerance in genetic programming
Future Generation Computer Systems
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
Visualization of genetic lineages and inheritance information in genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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With the exception of a small body of adaptive-parameter literature, evolutionary computation has traditionally favored keeping the population size constant through the course of the run. Unfortunately, genetic programming has an aging problem: for various reasons, late in the run the technique become less effective at optimization. Given a fixed number of evaluations, allocating many of them late in the run may thus not be a good strategy. In this paper we experiment with gradually decreasing the population size throughout a genetic programming run, in order to reallocate more evaluations to early generations. Our results show that over four problem domains and three different numbers of evaluations, decreasing the population size is always as good as, and frequently better than, various fixed-sized population strategies.