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dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2016-04-14T09:01:40Z
dc.date.available2016-04-14T09:01:40Z
dc.date.issued2015-07
dc.identifier.citationYang, S. (2015) Evolutionary computation for dynamic optimization problems. Proceedings of the Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, pp. 629-649en
dc.identifier.isbn9781450334884
dc.identifier.urihttp://hdl.handle.net/2086/11889
dc.descriptionThis is an invited tutorial on "Evolutionary Computation for Dynamic Optimization Problems", which was given at the 2015 Genetic and Evolutionary Computation Conference (GECCO 2015).en
dc.description.abstractMany real-world optimization problems are subject to dynamic environments, where changes may occur over time regarding optimization objectives, decision variables, and/or constraint conditions. Such dynamic optimization problems (DOPs) are challenging problems for researchers and practitioners in decision-making due to their nature of difficulty. Yet, they are important problems that decision-makers in many domains need to face and solve. Evolutionary computation (EC) is a class of stochastic optimization methods that mimic principles from natural evolution to solve optimization and search problems. EC methods are good tools to address DOPs due to their inspiration from natural and biological evolution, which has always been subject to changing environments. EC for DOPs has attracted a lot of research effort during the last twenty years with some promising results. However, this research area is still quite young and far away from well-understood. This tutorial aims to summarise the research area of EC for DOPs and attract potential young researchers into the important research area. It will provide an introduction to the research area of EC for DOPs and carry out an in-depth description of the state-of-the-art of research in the field regarding the following five aspects: benchmark problems and generators, performance measures, algorithmic approaches, theoretical studies, and applications. Some future research issues and directions regarding EC for DOPs will also be presented. The purpose is to (i) provide clear definition and classification of DOPs; (ii) review current approaches and provide detailed explanations on how they work; (iii) review the strengths and weaknesses of each approach; (iv) discuss the current assumptions and coverage of existing research on EC for DOPs; and (v) identify current gaps, challenges, and opportunities in EC for DOPs.en
dc.language.isoen_USen
dc.publisherACM Pressen
dc.subjectEvolutionary Computationen
dc.subjectDynamic Optimization Problemen
dc.titleEvolutionary Computation for Dynamic Optimization Problemsen
dc.typePresentationen
dc.identifier.doihttps://doi.org/10.1145/2739482.2756589
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedNoen
dc.explorer.multimediaNoen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.cclicenceCC-BY-NCen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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