A HYBRID APPROACH TO MODELING METABOLIC SYSTEMS USING A GENETIC ALGORITHM AND SIMPLEX-METHOD

One of the main obstacles in applying genetic algorithms (GA's) to com plex problems has been the high computational cost due to their slow c onvergence rate, We encountered such a difficulty in our attempt to us e the classical GA for estimating parameters of a metabolic model, To alleviate this difficulty, we developed a hybrid approach that combine s a GA with a stochastic variant of the simplex method in function opt imization, Our motivation for developing the stochastic simplex method is to introduce a cost-effective exploration component into the conve ntional simplex method. In an attempt to make effective use of the sim plex operation in a hybrid GA framework, we used an elite-based hybrid architecture that applies one simplex step to a top portion of the ra nked population, We compared our approach with five alternative optimi zation techniques including a simplex-GA hybrid independently develope d by Renders-Bersini (R-B) and adaptive simulated annealing (ASA), Our empirical evaluations showed that our hybrid approach for the metabol ic modeling problem outperformed all other techniques in terms of accu racy and convergence rate, We used two additional function optimizatio n problems to compare our approach with the five alternative methods, For a sin function maximization problem, our hybrid approach yields th e fastest convergence rate without sacrificing the accuracy of the sol ution found. For De Jong's F5 function minimization problem, our hybri d approach is the second best (next to ASA), Overall, these tests show ed that our hybrid approach is an effective and robust optimization te chnique. We further conducted an empirical study to identify major fac tors that affect the performance of the hybrid approach, The study ind icated that 1) our elite-based hybrid GA architecture contributes sign ificantly to the performance improvement and 2) the probabilistic simp lex is more cost-effective for our hybrid architecture than is the con ventional simplex, By analyzing the performance of the hybrid approach for the metabolic modeling problem, we hypothesized that the hybrid a pproach is particularly suitable for solving complex optimization prob lems the variables of which vary widely in their sensitivity to the ob jective function.