Copyright @ Springer-Verlag 2010.The Guest Editors acknowledge the research support by the Academy of Finland, Akatemiatutkija 130600, Algorithmic
Design Issues in Memetic Computing, and by the UK Engineering and Physical Sciences Research Council (EPSRC) Project: Evolutionary Algorithms for Dynamic Optimisation Problems, under Grant EP/E060722/1

Neri, F

Yang, S

Memetic Computing

English

Neri, Ferrante

Yang, Shengxiang

De Montfort University Open Research Archive

Guest editorial: Memetic computing in the presence of uncertainties

Brunel University Research Archive

Memetic Computing manuscript No.(will be inserted by the editor)Ferrante Neri · Shengxiang YangGuest Editorial: Thematic Issue on“Memetic Computing in the Presenceof Uncertainties”Received: date / Accepted: date1 Memetic computing in the presence of uncertaintiesThe complexity of the fitness landscapes, e.g., in terms of multi-modalitiesand presence of plateaus, resulting from real-world optimization problems,from one hand, and the limitations imposed by the No Free Lunch Theorem,from the other hand, suggested the diffusion of domain specific MemeticComputing approaches. More specifically, since the No Free Lunch Theoremproves that the performance of each algorithm over all the possible problemsis the same, it is clear there was no longer a reason to discuss which algorithmis universally better or worse. Thus, instead of trying to propose universallyapplicable algorithms, algorithmic designers started, in the recent years, topropose algorithms which are tailored to specific problems. In this context,the Memetic Computing paradigm offers the possibility of flexibly designingdomain specific optimization algorithms by integrating and coordinating al-gorithmic components capable to deal with the specific difficulties related tothe decision space and fitness landscape of a given problem.This thematic issue deals with a set of problem difficulties which are ofgreat interest in industrial contexts, i.e., uncertainties in the fitness func-This research was supported by the Academy of Finland, Akatemiatutkija 130600,Algorithmic Design Issues in Memetic Computing, and by the UK Engineering andPhysical Sciences Research Council (EPSRC) Project: Evolutionary Algorithms forDynamic Optimisation Problems, under Grant EP/E060722/1.F. NeriDepartment of Mathematical Information Technology, P.O. Box 35 (Agora), Uni-versity of Jyva¨skyla¨, FinlandE-mail: ferrante.neri@jyu.fiS. YangDepartment of Computer Science, University of Leicester, University Road, Leices-ter LE1 7RH, United KingdomE-mail: s.yang@mcs.le.ac.uk2tion, and gathers novel Memetic Computing approaches which attempt tosolve this set of problems. In other words, this thematic issue addressesthose optimization problems characterized by a complex fitness landscapeand uncertain environments. An optimization problem contains uncertaintieswhen, for a given point, the fitness value varies over time. In other words,the same solution can score differently if its fitness value is calculated indifferent moments of the evolution. Two macro-categories characterize un-certainties in optimization problems: 1) dynamic fitness environment, and2) noisy fitness function. The first means that the fitness landscape variesits shape, with the position of the optima, during the run-time. This situa-tion is typical, for example, in telecommunications and control engineering.The second means that the fitness function is non-deterministic but, on thecontrary, is affected by noise. This situation occurs when, for example, mea-surement devices and approximators are involved within the fitness calcula-tion/measurement/construction.2 Papers submitted and selected for this thematic issueThis thematic issue includes three novel studies, two of them on dynamicfitness landscapes and one on noisy optimization problems. The paper, titled“A predictive gradient strategy for multiobjective evolutionary algorithmsin a fast changing environment” and authored by Wee Tat Koo, Chi KeongGoh, and Kay Chen Tan, proposes a memetic approach composed of an evolu-tionary framework and a gradient search method for handling multiobjectiveoptimization problems in fast changing dynamic environments. The proposedalgorithm makes use of a prediction logic, which attempts to foresee the dy-namics of the landscape, and a memory structure which saves only the mostpromising solutions in order to reduce the computational overhead.In the paper “A memetic differential evolution approach in noisy opti-mization” by Ernesto Mininno and Ferrante Neri, a Differential Evolutionframework is hybridized with a line search attempting to locally optimizethe scale factor and thus the offspring performance. The algorithm also inte-grates a noise analysis component for performing, on the basis of a theoreticalbackground, only the necessary fitness evaluations which guarantee an effi-cient fitness averaging.In the paper “Dynamic function optimisation with hybridised extremaldynamics” by Irene Moser and Raymond Chiong, three memetic approachesfor moving peaks problems are presented. The three approaches differ fromeach other in the employed local search structure. In addition, this paperoffers a valuable comparative analysis on the role and the performance of thethree local search approaches for moving peaks problems.AcknowledgementThe Guest Editors would like to thank all authors who responded to the callfor papers and anonymous reviewers who offered their expertise and compe-tence in order to compose this thematic issue with a collection of important3works for this constantly growing niche of Memetic Computing. The GuestEditors would also like to thank the Editor-in-Chief, Assoc Prof. Meng HiotLim, for his professional attitude and outstanding dedication to the qualityof the journal.

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Guest editorial: Memetic computing in the presence of uncertainties

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