Using Meta-Genetic Algorithms to tune parameters of Genetic Algorithms to find lowest energy Molecular Conformers

Determining the electronic structure of long chain molecules is essential to the understanding of many biological processes, notably those involving molecular receptors in cells. Finding minimum energy conformers and thus electronic structure of long-chain molecules by exhaustive search quickly becomes infeasible as the chain length increases. Typically, resources required are proportional to the number of possible conformers (shapes), which scales as O(3∧L) where L is the length. An optimized genetic algorithm that can determine the minimum energy conformer of an arbitrary long-chain molecule in a feasible time is described, using the tool, PyEvolve. The method is to first solve a generic problem for a long chain by exhaustive search, then by using the pre-determined results in a look-up table, to make use of a Meta-GA to optimize parameters of a simple GA through an evolutionary process to solve that same problem. By comparing the results using the tuned parameters obtained by this method with the results from exhaustive search on several molecules of comparable chain length we have obtained quantitative measurements of an increase in speed by a factor of three over standard parameter settings, and a factor of ten over exhaustive search

TUNING OPTIMIZATION SOFTWARE PARAMETERS FOR MIXED INTEGER PROGRAMMING PROBLEMS

The tuning of optimization software is of key interest to researchers solving mixed integer programming (MIP) problems. The efficiency of the optimization software can be greatly impacted by the solver’s parameter settings and the structure of the MIP. A designed experiment approach is used to fit a statistical model that would suggest settings of the parameters that provided the largest reduction in the primal integral metric. Tuning exemplars of six and 59 factors (parameters) of optimization software, experimentation takes place on three classes of MIPs: survivable fixed telecommunication network design, a formulation of the support vector machine with the ramp loss and L1-norm regularization, and node packing for coding theory graphs. This research presents and demonstrates a framework for tuning a portfolio of MIP instances to not only obtain good parameter settings used for future instances of the same class of MIPs, but to also gain insights into which parameters and interactions of parameters are significant for that class of MIPs. The framework is used for benchmarking of solvers with tuned parameters on a portfolio of instances. A group screening method provides a way to reduce the number of factors in a design and reduces the time it takes to perform the tuning process. Portfolio benchmarking provides performance information of optimization solvers on a class with instances of a similar structure