Results 11 - 20 of 19,316

A Multi-Objective Evolutionary Algorithm for Portfolio

by Noël-ann Bradshaw, Chris Walshaw, Constantinos Ierotheou, A. Kevin Parrott
"... Abstract. The use of heuristic evolutionary algorithms to address the problem of portfolio optimisation has been well documented. In order to decide which assets to invest in and how much to invest, one needs to assess the potential risk and return of different portfolios. This problem is ideal for ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
for solving using a Multi-Objective Evolutionary Algorithm (MOEA) that maximises return and minimises risk. We are working on a new MOEA loosely based on Zitzler’s Strength Pareto Evolutionary Algorithm (SPEA2) [20] using Value at Risk (VaR) as the risk constraint. This algorithm currently uses a dynamic

A Multi-Objective Evolutionary Algorithm Approach

by For Crusher Optimisation, L. While, L. Barone, P. Hingston, S. Huband, D. Tuppurainen, R. Bearman - Minerals Engineering , 2004
"... The performance of crushing equipment in mineral processing circuits is often critical to the generation of final product. A multi-objective evolutionary algorithm has been developed that allows the crusher internal geometry to be created and evaluated against multiple performance objectives. The ..."
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The performance of crushing equipment in mineral processing circuits is often critical to the generation of final product. A multi-objective evolutionary algorithm has been developed that allows the crusher internal geometry to be created and evaluated against multiple performance objectives

Quality-Time Analysis of Multi-Objective Evolutionary Algorithms

by Jian-hung Chen, Shinn-ying Ho, David E. Goldberg
"... A quality-time analysis of multi-objective evolutionary algorithms (MOEAs) based on schema theorem and building blocks hypothesis is developed. A bicriteria OneMax problem, a hypothesis of niche and species, and a definition of dissimilar schemata are introduced for the analysis. In this paper, the ..."
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A quality-time analysis of multi-objective evolutionary algorithms (MOEAs) based on schema theorem and building blocks hypothesis is developed. A bicriteria OneMax problem, a hypothesis of niche and species, and a definition of dissimilar schemata are introduced for the analysis. In this paper

Self-Adaptive Mechanism for Multi-objective Evolutionary Algorithms

by Fanchao Zeng, Malcolm Yoke, Hean Low, James Decraene, Suiping Zhou, Wentong Cai
"... Abstract—Evolutionary algorithms can efficiently solve multi-objective optimization problems (MOPs) by obtaining diverse and near-optimal solution sets. However, the performance of multi-objective evolutionary algorithms (MOEAs) is often limited by the suitability of their corresponding parameter se ..."
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Abstract—Evolutionary algorithms can efficiently solve multi-objective optimization problems (MOPs) by obtaining diverse and near-optimal solution sets. However, the performance of multi-objective evolutionary algorithms (MOEAs) is often limited by the suitability of their corresponding parameter

Decomposition of Multi-Objective Evolutionary Algorithm based on Estimation of Distribution

by Jian-qiu Zhang, Feng Xu, Xian-wen Fang , 2014
"... Abstract: Decomposition of multi-objective evolutionary algorithm has better distribution, but the number of groups will increase dramatically as the target number increases, seriously affecting the efficiency of the algorithm. This paper presents a decomposition of multi-objective evolutionary algo ..."
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Abstract: Decomposition of multi-objective evolutionary algorithm has better distribution, but the number of groups will increase dramatically as the target number increases, seriously affecting the efficiency of the algorithm. This paper presents a decomposition of multi-objective evolutionary

A Unified Model for Multi-Objective Evolutionary Algorithms with Elitism

by Marco Laumanns, Eckart Zitzler, Lothar Thiele - In Congress on Evolutionary Computation (CEC 2000 , 2000
"... Though it has been claimed that elitism could improve evolutionary multi-objective search significantly, a thorough and extensive evaluation of its effects is still missing. Guidelines on how elitism could successfully be incorporated have not yet been developed. This paper presents a unified model ..."
Abstract - Cited by 42 (6 self) - Add to MetaCart
of multi-objective evolutionary algorithms, in which arbitrary variation and selection operators can be combined as building blocks, including archiving and re-insertion strategies. The presented model enables most specific multi-objective (evolutionary) algorithm to be formulated as an instance of it

FLOCK 1 DATA MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR TURBULENT FLOW DETECTION

by Maged Marghany
"... ABSTRACT:This study has demonstrated a design tool for turbulent flow detection in Flock 1 data using Multi-Objective Evolutionary Algorithm which based on Pareto optimal solutions. The Flock 1 data along the Suez ..."
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ABSTRACT:This study has demonstrated a design tool for turbulent flow detection in Flock 1 data using Multi-Objective Evolutionary Algorithm which based on Pareto optimal solutions. The Flock 1 data along the Suez

Runtime Analyses for a Simple Multi-objective Evolutionary Algorithm

by Oliver Giel , 2003
"... Evolutionary algorithms are not only applied to optimization problems where a single objective is to be optimized but also to problems where several and often conflicting objectives are to be optimized simultaneously. Practical knowledge on the design and application of multi-objective evolution ..."
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Evolutionary algorithms are not only applied to optimization problems where a single objective is to be optimized but also to problems where several and often conflicting objectives are to be optimized simultaneously. Practical knowledge on the design and application of multi-objective

A multi-objective evolutionary algorithm based on decomposition

by Qingfu Zhang, Hui Li - IEEE Transactions on Evolutionary Computation, Accepted , 2007
"... 1 Decomposition is a basic strategy in traditional multiobjective optimization. However, this strategy has not yet widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a MOP into a number o ..."
Abstract - Cited by 44 (14 self) - Add to MetaCart
1 Decomposition is a basic strategy in traditional multiobjective optimization. However, this strategy has not yet widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a MOP into a number

Convergence Properties of Some Multi-Objective Evolutionary Algorithms

by Günter Rudolph, Alexandru Agapie - IN CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2000 , 2000
"... We present four abstract evolutionary algorithms for multiobjective optimization and theoretical results that characterize their convergence behavior. Thanks to these results it is easy to verify whether a particular instantiation of these abstract evolutionary algorithms offers the desired limit b ..."
Abstract - Cited by 41 (7 self) - Add to MetaCart
We present four abstract evolutionary algorithms for multiobjective optimization and theoretical results that characterize their convergence behavior. Thanks to these results it is easy to verify whether a particular instantiation of these abstract evolutionary algorithms offers the desired limit
Results 11 - 20 of 19,316