An approximate algorithm for estimating treatment lags from right censored data

The problem of likelihood estimate of a function, given limited data and a set of constraints, finds many practical applications. One of them is the lag function estimation for treatment effects in clinical trials. However, searching for the global optimum in such problems often turns out to be prohibitively complex. In this paper, we present an algorithm that can be effectively used to approximate such solutions. This algorithm modifies standard genetic algorithms, which model the natural processes of information inheritance and selective pressure. The most important modifications deal with the complex strong constraints imposed on the sought solution. We present few experiments with a simplified version of the problem and discuss possible future extensions that relax the restrictions.