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» On the Brittleness of Evolutionary Algorithms
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GECCO
2006
Springer
188views Optimization» more  GECCO 2006»
15 years 1 months ago
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
This work describes a forward-looking approach for the solution of dynamic (time-changing) problems using evolutionary algorithms. The main idea of the proposed method is to combi...
Iason Hatzakis, David Wallace
77
Voted
PR
2008
131views more  PR 2008»
14 years 10 months ago
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks. ...
Salvador García, José Ramón C...
GECCO
2007
Springer
174views Optimization» more  GECCO 2007»
15 years 4 months ago
Investigating data-flow coverage of classes using evolutionary algorithms
It is not unusual for a software development organization to expend 40 percent of total project effort on testing, which can be a very laborious and time-consuming process. Thus, ...
Konstantinos Liaskos, Marc Roper, Murray Wood
CEC
2005
IEEE
15 years 3 months ago
A model-based evolutionary algorithm for bi-objective optimization
Abstract- The Pareto optimal solutions to a multiobjective optimization problem often distribute very regularly in both the decision space and the objective space. Most existing ev...
Aimin Zhou, Qingfu Zhang, Yaochu Jin, Edward P. K....
GECCO
2005
Springer
130views Optimization» more  GECCO 2005»
15 years 3 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