Genetic Algorithm Search

Scoring function

Consider a set S = ( g1, , g2, ..., gk ) of k genes.
scoringfunction.gif
where di: the statistic of gene i, μ: the mean of statistic of all background genes.

Search algorithm

A GA is a heuristic search algorithm inspired by natural process of evolution, “survival of the fittest”. The evolution begins with a population, a set of random chromosomes. In each generation, the fitness of every chromosome in the population is evaluated though a fitness function, and then some are selected to undergo further modification: ‘crossover’ and ‘mutation’. The elitism allows certain number of fittest individuals to be survived for the next generation. This process is repeated until certain termination conditions are satisfied. The typical GA flow is depicted in Figure 1.
figure1.png
Figure 1. Flowchart of the GA used to find the phenotype-responsive sub-networks.





Last edited Feb 1, 2013 at 5:06 PM by yongkeecho, version 6

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