Iterative Prototype Optimisation with Evolved Improvement Steps

Ji Kubalk and Jan FaiglCzech Technical UniversityDepartment of CyberneticsTechnicka 2, 166 27, Prague, Czech Republicemail: {kubalik,xfaigl}@labe.felk.cvut.cz

Iter. Prototype Opt. with Evolved Imp. Steps

MotivationTypically, evolutionary optimisation framework considers an evolution of a population of candidate solutions to the problem at handcandidate solution encodes a complete solution (a complete set of problem control parameters, a complete schedule in JSP, a complete tour for TSP, etc.)the optimal or well-fit solution is hard to find for large problem instancesHere, EA does not handle the solved problem as a wholeEA is employed within the iterative optimisation frameworkits role is to evolve the best modification of the current solution prototype in each iteration

Iter. Prototype Opt. with Evolved Imp. Steps

Outline of POEMS algorithm generate(Prototype);repeatBestSequence run_EA(Prototype);if(apply_to(BestSequence, Prototype) is_better_than Prototype);then Prototype apply_to(BestSequence, Prototype);until(POEMS termination condition);return Prototype;

Prototype initialised randomly or using some problem-specific knowledge

Iter. Prototype Opt. with Evolved Imp. Steps

Implementation IssuesAction Sequence RepresentationActions set of primitive actionsdetermines the explorative power of the algorithmlinear chromosomes of maximal length MaxGenes