Accession Number : ADA260155
Title : Boltzmann Weighted Selection Improves Performance of Genetic Algorithms
Descriptive Note : Memorandum rept.
Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
Personal Author(s) : Maza, Michael de la ; Tidor, Bruce
Report Date : Dec 1991
Pagination or Media Count : 20
Abstract : We have implemented Boltzmann scaling on the optimization function to select the number of offspring each individual in the current population contributes to the next generation; the procedure outperforms a standard proportional scaling method on the small set of problems we have investigated. A broader range of problems should be used to test the generality of this result. The tolerance schedule is robust enough that the same schedule was used successfully for problems of different sizes and correspondingly different scales in optimization space. These results show that, for the molecular biology problem, many Boltzmann experiments completed with a correct solution before the decrease in tolerance that occurred after generation 10 and nearly all completed before the schedule leveled off again after generation 40.
Descriptors : *ALGORITHMS , *OPTIMIZATION , *MOLECULAR BIOLOGY , *FUNCTIONS(MATHEMATICS) , *BOLTZMANN EQUATION , *GENETICS , WEIGHTING FUNCTIONS , SCALING FACTOR , SELECTION , TOLERANCE , CONVERGENCE , STANDARDIZATION , ARTIFICIAL INTELLIGENCE
Subject Categories : Genetic Engineering and Molecular Biology
Distribution Statement : APPROVED FOR PUBLIC RELEASE