Using adaptive operators in genetic search

Authors:
Jonatan Gómez;Dipankar Dasgupta;Fabio González
Affiliations:
The University of Memphis, Memphis, TN and Universidad Nacional de Colombia, Bogotá, Colombia;The University of Memphis, Memphis, TN;The University of Memphis, Memphis, TN and Universidad Nacional de Colombia, Bogotá, Colombia
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

Citing 4
Cited 2

Adapting Operator Probabilities in Genetic Algorithms

Proceedings of the 3rd International Conference on Genetic Algorithms
What Have You Done for Me Lately? Adapting Operator Probabilities in a Steady-State Genetic Algorithm

Proceedings of the 6th International Conference on Genetic Algorithms
On the Application of Cohort-Driven Operators to Continuous Optimization Problems Using Evolutionary Computation

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Adapting operator settings in genetic algorithms

Evolutionary Computation

Differential evolution algorithm with strategy adaptation for global numerical optimization

IEEE Transactions on Evolutionary Computation
Differential evolution algorithm with ensemble of parameters and mutation strategies

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we provided an extension of our previous work on adaptive genetic algorithm [1]. Each individual encodes the probability (rate) of its genetic operators. In every generation, each individual is modified by only one operator. This operator is selected according to its encoded rates. The rates are updated according to the performance achieved by the offspring (compared to its parents) and a random learning rate. The proposed approach is augmented with a simple transposition operator and tested on a number of benchmark functions.