A Gibbs distribution that learns from GA dynamics

Details A Gibbs distribution that learns from GA dynamics A Gibbs distribution that learns from GA dynamics

2010-04-21

arxiv.org/abs/1004.3725v1

Computer Science

Neural and Evolutionary Computing

14 pages, 19 figures

Scientific paper

A general procedure of average-case performance evaluation for population dynamics such as genetic algorithms (GAs) is proposed and its validity is numerically examined. We introduce a learning algorithm of Gibbs distributions from training sets which are gene configurations (strings) generated by GA in order to figure out the statistical properties of GA from the view point of thermodynamics. The learning algorithm is constructed by means of minimization of the Kullback-Leibler information between a parametric Gibbs distribution and the empirical distribution of gene configurations. The formulation is applied to the solvable probabilistic models having multi-valley energy landscapes, namely, the spin glass chain and the Sherrington-Kirkpatrick model. By using computer simulations, we discuss the asymptotic behaviour of the effective temperature scheduling and the residual energy induced by the GA dynamics.

Affiliated with

Also associated with

No associations

Rating

A Gibbs distribution that learns from GA dynamics does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with A Gibbs distribution that learns from GA dynamics, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Gibbs distribution that learns from GA dynamics will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-327327