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CEC
2010
IEEE
9 years 9 months ago
Multi-objective robust static mapping of independent tasks on grids
We study the problem of efficiently allocating incoming independent tasks onto the resources of a Grid system. Typically, it is assumed that the estimated time to compute each task...
Bernabé Dorronsoro Díaz, Pascal Bouv...
BIOSYSTEMS
2007
72views more  BIOSYSTEMS 2007»
10 years 2 months ago
On the use of multi-objective evolutionary algorithms for survival analysis
This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approxima...
Christian Setzkorn, Azzam Fouad George Taktak, Ber...
CEC
2010
IEEE
10 years 3 months ago
A parallel framework for multi-objective evolutionary optimization
This work focuses on the development of a parallel framework method to improve the effectiveness and the efficiency of the obtained solutions by Multi-objective Evolutionary Algori...
Dipankar Dasgupta, David Camilo Becerra Romero, Al...
GECCO
2006
Springer
130views Optimization» more  GECCO 2006»
10 years 6 months ago
An efficient multi-objective evolutionary algorithm with steady-state replacement model
The generic Multi-objective Evolutionary Algorithm (MOEA) aims to produce Pareto-front approximations with good convergence and diversity property. To achieve convergence, most mu...
Dipti Srinivasan, Lily Rachmawati
GECCO
2003
Springer
10 years 8 months ago
HEMO: A Sustainable Multi-objective Evolutionary Optimization Framework
The capability of multi-objective evolutionary algorithms (MOEAs) to handle premature convergence is critically important when applied to real-world problems. Their highly multi-mo...
Jianjun Hu, Kisung Seo, Zhun Fan, Ronald C. Rosenb...
GECCO
2004
Springer
108views Optimization» more  GECCO 2004»
10 years 8 months ago
Simple Population Replacement Strategies for a Steady-State Multi-objective Evolutionary Algorithm
This paper explores some simple evolutionary strategies for an elitist, steady-state Pareto-based multi-objective evolutionary algorithm. The experimental framework is based on the...
Christine L. Mumford
GECCO
2005
Springer
130views Optimization» more  GECCO 2005»
10 years 8 months ago
Quality-time analysis of multi-objective evolutionary algorithms
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 hypo...
Jian-Hung Chen, Shinn-Ying Ho, David E. Goldberg
EMO
2005
Springer
123views Optimization» more  EMO 2005»
10 years 8 months ago
Initial Population Construction for Convergence Improvement of MOEAs
Nearly all Multi-Objective Evolutionary Algorithms (MOEA) rely on random generation of initial population. In large and complex search spaces, this random method often leads to an ...
Christian Haubelt, Jürgen Gamenik, Jürge...
CEC
2007
IEEE
10 years 9 months ago
Incrementally maximising hypervolume for selection in multi-objective evolutionary algorithms
— Several multi-objective evolutionary algorithms compare the hypervolumes of different sets of points during their operation, usually for selection or archiving purposes. The ba...
Lucas Bradstreet, R. Lyndon While, Luigi Barone
GECCO
2009
Springer
167views Optimization» more  GECCO 2009»
10 years 9 months ago
Fixed-parameter evolutionary algorithms and the vertex cover problem
In this paper, we consider multi-objective evolutionary algorithms for the Vertex Cover problem in the context of parameterized complexity. We relate the runtime of our algorithms...
Stefan Kratsch, Frank Neumann
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