Operator self-adaptation in genetic programming

Authors:
Min Hyeok Kim;Robert Ian McKay;Nguyen Xuan Hoai;Kangil Kim
Affiliations:
Seoul National University, Korea;Seoul National University, Korea;Hanoi University, Vietnam;Seoul National University, Korea
Venue:
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Year:
2011

Citing 11
Cited 2

Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding

Machine Learning - Special issue on genetic algorithms
Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Numerical Optimization of Computer Models

Numerical Optimization of Computer Models
Some Experimental Results with Tree Adjunct Grammar Guided Genetic Programming

EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
An adaptive pursuit strategy for allocating operator probabilities

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Adapting operator settings in genetic algorithms

Evolutionary Computation
Extreme Value Based Adaptive Operator Selection

Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Parameter Setting in Evolutionary Algorithms

Parameter Setting in Evolutionary Algorithms
Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms

Learning and Intelligent Optimization
Tree adjunct grammars

Journal of Computer and System Sciences
Representation and structural difficulty in genetic programming

IEEE Transactions on Evolutionary Computation

Evolutionary operator self-adaptation with diverse operators

EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We investigate the application of adaptive operator selection rates to Genetic Programming. Results confirm those from other areas of evolutionary algorithms: adaptive rate selection out-performs non-adaptive methods, and among adaptive methods, adaptive pursuit out-performs probability matching. Adaptive pursuit combined with a reward policy that rewards the overall fitness change in the elite worked best of the strategies tested, though not uniformly on all problems.