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Title: Multi objective evolutionary optimization in uncertain environments
Keywords: noise, optimization, evolutionary algorithms, stochastic, multi objective
Issue Date: 14-Jul-2011
Citation: CHIA JUN YONG (2011-07-14). Multi objective evolutionary optimization in uncertain environments. ScholarBank@NUS Repository.
Abstract: The decisions that we make in our daily lives is the cumulative result of complex optimization processes that goes on as the neurons in our head fire away. We can observe the subtle cues of optimization even in the simple task of getting from point A to point B. We optimize time and money by choosing the fastest and cheapest means of transport to point B (example taking a taxi). The decision to take taxi can be clouded by the uncertainties that come with it. Taxi arrival timings usually are not precise and follow a Poisson distribution. Other foreseeable uncertainties such as traffic jams and vehicle break down have to be considered too. We witnessed how we subconsciously make decision on the go based on the new knowledge acquired and how we make use of this new knowledge to reconfigure our optimization on the go. Spontaneous and simultaneous optimizations subjected to dynamicity of the problems happen all the time in our lives. The same can be said for industrial processes and other complex problems, where uncertainties are an integral part of multi objective optimization processes. In order to gain a better understanding of the effects and characteristics of uncertainties, this work attempts to study the dynamics and effects of noise before attempting to tackle the noisy dynamic real life problems. The first part of this work focuses on the investigation of a proposed noise handling technique. The proposed technique makes use of a Data Mining operator to collect aggregated information to direct the search amidst noise. The idea is to make use of the aggregation of data collected from the population to negate the influence of noise through explicit averaging. The proposed operator will be progressively tested on noiseless single and multi objectives problems and finally implemented on noisy multi objective problems for completeness of investigation. The second part of this work will pursue the uncertainties related to dynamic multi objective optimization of financial engineering problems. The dynamicity of the financial drives the rationale behind rebalancing strategies for passive fund management. Portfolios rebalancing are performed to take into account new market conditions, new information and existing positions. The rebalancing can be either sparked by specific criteria based trigger or executed periodically. This work considers the different rebalancing strategies and investigates their influences on the overall tracking performance. The proposed multi period framework will provide insights into the evolution of the composition of the portfolios with respect to the chosen rebalancing strategy.
Appears in Collections:Master's Theses (Open)

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