Memetic Algorithms for molecular conformation and other optimization problems

MemeticAlgorithmsformolecularconformationandotheroptimizationproblemsPablo MoscatoDepartamento de Engenharia de Sistemas,Faculdade de Engenharia Eletrica, Universidade Estadual de Campinas,C.P. 6101, Campinas, SP, CEP 13081-970, BRAZILe-mail:moscato@densis.fee.unicamp.brWWW:http://www.densis.fee.unicamp. br/~moscatoJune 29, 1998With the name of `Memetic Algorithms' we recognize a class of metaheuristics which constitutesanemergingparadigm foroptimization.Formore thanadecadenowithasb eenapplied withsuccesstoalargevarietyofcombinatorial andnonlinearproblems.Inthelastfourears,andmostly in the last two, several techniquesintro duced in molecular optimization problems can b echaracterizedinthisway.Wetrytojusttro duceherethenewtechniqueswhileseekemphasize the relation with previous and current work in Memetic Algorithms and Scatter Search.MemeticAlgorithms (MAs)isap opulation-basedapproachtooptimization [28]. Itcanb eapplied b oth to nonlinear and discrete (combinatorial) optimization problems.They are orders ofmagnitude faster than Genetic Algorithms in some problem domains. We call it a `metaheuristic'sinceit is a general purp osestrategy that guides other basic heuristicsor truncatedexact meth-o ds.Other metaheuristics, like Simulated Annealing (SA) or Guided Lo cal Search (GLS) (whichguidesome underlying\Hill-climbing" pro cedure),areappliedtoasingle\optimizer" andthustheycannotb ecategorizedas`p opulation-based'. BasicTabu Search(TS)techniquesalso usea single optimizer, but more evolved metaheuristics like Scatter Search (SS), also use pro ceduresbased in \multiple-p oint" optimization. Genetic Algorithms (GAs), also try to evolve solutions incon gurationspacebyusinga \p opulation" of individuals whichrepresentalternative solutionsof theproblems.Go o d reviewsstressingthesimilarities and di erencesof theseapproachescanb e found in [10] [11] and [28] (the latter, written in 1990-92, anticipates the relevance of MAs inprotein landscap es). A main di erenceb etweenGAs and MAs is that thelatter approach triesto use all p ossibleknowledge available to solve the problem.Some SS pro cedurescan b eb eclassi ed as MAsto o,though none of theapproaches prop erly includesthe other.In essence,MAsconstitutean exer-ciseofhumblenessandcommon sense;if severalalternativealgorithms fortheproblem alreadyexist, it should b ea wise idea to use them together.This is of particular interestfor the practi-tioner, who has b een relying on a well-known co de which enables to give lo cally optimal solutionsbutrequiressome extra global optimization (adding sto chasticity to thesearch). Thus MAsaresometimes called\Hybrid Genetic Algorithms", or\Lamarkian Evolutionary Algorithms"or even\knowledge-augmented GAs". Otherdenominations include\Genetic Lo cal Search"or\ParallelGenetic Algorithms". They are easy to b e recognized from the so-called \standard GAs" since inMAsthenewcon gurations(children)arecreatedfrom highly evolved\parents"from previousgenerations.The indep endent evolution of parents is done using knowledge ab out the problem. Innon-linear settings this generally achieved using gradient information. In combinatorial optimiza-tion problems they generally use some lo cal search or constructive algorithm but truncated exactpro cedures(like truncated branch-and-b ound or branch-and-cut techniques) can also b e used.Inall cases the existence of theorems and prop erties related to the structureof the optimum con g-uration greatly help to reduce the whole search pro cess.1