4,263 research outputs found
The Novel Approach of Adaptive Twin Probability for Genetic Algorithm
The performance of GA is measured and analyzed in terms of its performance
parameters against variations in its genetic operators and associated
parameters. Since last four decades huge numbers of researchers have been
working on the performance of GA and its enhancement. This earlier research
work on analyzing the performance of GA enforces the need to further
investigate the exploration and exploitation characteristics and observe its
impact on the behavior and overall performance of GA. This paper introduces the
novel approach of adaptive twin probability associated with the advanced twin
operator that enhances the performance of GA. The design of the advanced twin
operator is extrapolated from the twin offspring birth due to single ovulation
in natural genetic systems as mentioned in the earlier works. The twin
probability of this operator is adaptively varied based on the fitness of best
individual thereby relieving the GA user from statically defining its value.
This novel approach of adaptive twin probability is experimented and tested on
the standard benchmark optimization test functions. The experimental results
show the increased accuracy in terms of the best individual and reduced
convergence time.Comment: 7 pages, International Journal of Advanced Studies in Computer
Science and Engineering (IJASCSE), Volume 2, Special Issue 2, 201
Use of Statistical Outlier Detection Method in Adaptive\ud Evolutionary Algorithms
In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to\ud
adaptive methods and soundly outperforms the non-adaptive\ud
case
Use of statistical outlier detection method in adaptive evolutionary algorithms
In this paper, the issue of adapting probabilities for Evolutionary Algorithm
(EA) search operators is revisited. A framework is devised for distinguishing
between measurements of performance and the interpretation of those
measurements for purposes of adaptation. Several examples of measurements and
statistical interpretations are provided. Probability value adaptation is
tested using an EA with 10 search operators against 10 test problems with
results indicating that both the type of measurement and its statistical
interpretation play significant roles in EA performance. We also find that
selecting operators based on the prevalence of outliers rather than on average
performance is able to provide considerable improvements to adaptive methods
and soundly outperforms the non-adaptive case
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