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Multiobjective Robustness for Portfolio Optimization in Volatile Environments ABSTRACT
"... Multiobjective methods are ideal for evolving a set of portfolio optimisation solutions that span a range from highreturn/highrisk to lowreturn/lowrisk, and an investor can choose her preferred point on the riskreturn frontier. However, there are no guarantees that a lowrisk solution will remai ..."
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Multiobjective methods are ideal for evolving a set of portfolio optimisation solutions that span a range from highreturn/highrisk to lowreturn/lowrisk, and an investor can choose her preferred point on the riskreturn frontier. However, there are no guarantees that a lowrisk solution will remain lowrisk — if the environment changes, the relative positions of previously identified solutions may alter. A lowrisk solution may become highrisk and vice versa. The robustness of a Multiobjective Genetic Programming (MOGP) algorithm such as SPEA2 is vitally important in the context of the realworld problem of portfolio optimisation. We explore robustness in this context, providing new definitions and a statistical measure to quantify the robustness of solutions. A new robustness measure is incorporated into a MOGP fitness function to bias evolution towards more robust solutions. This new system (“RSPEA2”) is compared against the original SPEA2 and we present our results.
Hybridisation of the multiobjective evolutionary algorithms and the gradientbased algorithms
 IEEE CEC’2003 Congress on Evolutionary Computation
, 2003
"... This paper proposes a new multiobjective evolutionary algorithm, called neighborhood exploring evolution strategy (NEES). This approach incorporates the idea of neighborhood exploration together with other techniques commonly used in the multiobjective evolutionary optimization literature (namely, ..."
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This paper proposes a new multiobjective evolutionary algorithm, called neighborhood exploring evolution strategy (NEES). This approach incorporates the idea of neighborhood exploration together with other techniques commonly used in the multiobjective evolutionary optimization literature (namely, nondominated sorting and diversity preservation mechanisms). This idea of the proposed approach was derived from a singleobjective evolutionary algorithm, called lineup competition algorithm (LCA). The main idea is to assign neighborhoods of different size to different solutions. Within each neighborhood, new solutions are generated using a (1+ λ)ES (evolution strategy). This scheme naturally balances the effect of local search (which is done by the evolution strategy) with that of the global search performed by the algorithm, and gradually impels the population to progress towards the true Paretooptimal front of the problem and to explore the extent of such front. Three versions of our proposal are studied: a (1+1)NEES, a (1+2)NEES and a (1+5)NEES. Such approaches are validated on a set of standard test problems reported in the specialized literature. Simulation results indicate that, for continuous numerical optimization problems, our proposal (particularly the (1+1)NEES) is competitive with respect to the NSGAII, which is an algorithm representative of the stateoftheart in evolutionary multiobjective optimization. Moreover, all the versions of our NEES improve on the results of the NSGAII when dealing with a discrete optimization problem. Although preliminary, such results might indicate a potential application area in which our proposed approach could be particularly useful. Keywords: Neighborhood exploring evolution strategy; Lineup competition algorithm; multiobjective optimization; evolutionary algorithm; evolution strategy 1
An optimization algorithm inspired by musical composition in constrained optimization problems
, 2013
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Contents lists available at ScienceDirect Computer Communications
"... journal homepage: www.elsevier.com/locate/comcom Maximizing network lifetime based on transmission range adjustment ..."
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journal homepage: www.elsevier.com/locate/comcom Maximizing network lifetime based on transmission range adjustment
Mitigating Energy Holes Based on Transmission Range Adjustment in Wireless Sensor Networks
"... In a wireless sensor network (WSN), the energy hole problem is a key factor which affects the lifetime of the networks. In a WSN with circular multihop deployment (modeled as concentric coronas), sensors in one corona have the same transmission range termed as the transmission range of this corona, ..."
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In a wireless sensor network (WSN), the energy hole problem is a key factor which affects the lifetime of the networks. In a WSN with circular multihop deployment (modeled as concentric coronas), sensors in one corona have the same transmission range termed as the transmission range of this corona, and different coronas have different transmission ranges, which compose a list termed as transmission range list. Based on our improved corona model with levels, we propose that a right transmission range of each corona is the decision factor for optimizing network lifetime after nodes deployment. We prove that searching optimal transmission range lists is a multiobjective optimization problem (MOP), which is NP hard. We propose a centralized algorithm and a distributed algorithm to build the transmission range list for different node distributions. The two algorithms can not only reduce the searching complexity but also obtain results approximated to the optimal solution. Furthermore, the simulation results indicate that the network lifetime under our solution approximates to that ensured by the optimal list. Compared with existing algorithms, our solution can make the network lifetime be extended more than two times longer.
Solving Graph Coloring Problems Using Cultural Algorithm
"... This paper proposes a new method based on the cultural algorithm to solve graph coloring problem (GCP). Graph coloring problem involves finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. In this paper various components of cultural ..."
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This paper proposes a new method based on the cultural algorithm to solve graph coloring problem (GCP). Graph coloring problem involves finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. In this paper various components of cultural algorithm have been implemented to solve GCP with a self adaptive behavior in an efficient manner. As a result of utilizing the cultural algorithm, the proposed algorithm results on finding the solution significantly fast. The experimental results show that the proposed algorithm has a high performance in solving this problem.
A Novel Culture Algorithm for Knowledge Integration
"... Abstract—Integrating knowledge from different subjects and sources to obtain an effective knowledge base is the key to improve decision quality and enhance organizational core competency. We put forward a knowledge integration strategy under the framework of culture algorithm. It encodes the knowled ..."
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Abstract—Integrating knowledge from different subjects and sources to obtain an effective knowledge base is the key to improve decision quality and enhance organizational core competency. We put forward a knowledge integration strategy under the framework of culture algorithm. It encodes the knowledge uniformly and through the evolvement of the two phases of population space and belief space the communication protocol is established among the two spaces. Then an effective and concise knowledge base is automatically produced without any knowledge of specific fields. The experiment shows that comparing with traditional genetic algorithm, the model can classify the knowledge more precisely, reduce redundant knowledge and remove contradictory knowledge.
Econometrics and Metaheuristic Optimization Approaches to International Portfolio Diversification
, 2012
"... Using advanced techniques of econometrics and a metaheuristic optimization approach, this study attempts to evaluate the potential advantages of international portfolio diversification for East Asian international investors when investing in the Middle Eastern emerging markets. Overall, the results ..."
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Using advanced techniques of econometrics and a metaheuristic optimization approach, this study attempts to evaluate the potential advantages of international portfolio diversification for East Asian international investors when investing in the Middle Eastern emerging markets. Overall, the results of both econometric and the metaheuristic optimization methods are supporting each other. Findings of this study highlight the potential role of the Middle Eastern equity markets in providing international portfolio diversification benefits for East Asian investors. It is also found that the long and the shortterm efficient frontiers in any of the intra or interregionally diversified portfolios do not provide similar benefits. Keywords: International portfolio optimization, Multiplefunction genetic algorithm, Integration,
HeriotWatt University
"... Abstract — Genetic programming (GP) is increasingly investigated in finance and economics. One area of study is its use to discover effective rules for technical trading in the context of a portfolio of equities (or an index). Early work in this area used GP to find rules that were profitable, but w ..."
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Abstract — Genetic programming (GP) is increasingly investigated in finance and economics. One area of study is its use to discover effective rules for technical trading in the context of a portfolio of equities (or an index). Early work in this area used GP to find rules that were profitable, but were nevertheless outperformed by the simple “buy and hold ” (B&H) strategy. Attempts since then tend to report similar findings, except for a handful of cases where GP methods have been found to outperform B&H. Recent work has clarified that robust outperformance of B&H depends on, mainly, the adoption of a relatively infrequent trading strategy (e.g. monthly), as well as a range of factors that amount to sound engineering of the GP grammar and the validation strategy. Here we add a comprehensive study of multiobjective approaches to this investigation, and find that multiobjective strategies provide even more robustness in outperforming B&H, even in the context of more frequent (e.g. weekly) trading decisions. Keywordsfinancial trading; genetic programming; multiobjective algorithms I.
Fatigue life Modeling and Prediction of GRP Composites Using Multiobjective Evolutionary Optimized Neural Networks
 INTERNATIONAL JOURNAL OF MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES
, 2012
"... In this article, Evolutionary Algorithms (EAs) are used ..."