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GECCO
2005
Springer
132views Optimization» more  GECCO 2005»
16 years 1 hour ago
Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem
Augmenting an evolutionary algorithm with knowledge of its target problem can yield a more effective algorithm, as this presentation illustrates. The Quadratic Knapsack Problem e...
Bryant A. Julstrom
GECCO
2004
Springer
15 years 12 months ago
Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World
This paper describes a continuous estimation of distribution algorithm (EDA) to solve decomposable, real-valued optimization problems quickly, accurately, and reliably. This is the...
Chang Wook Ahn, Rudrapatna S. Ramakrishna, David E...
GECCO
2004
Springer
160views Optimization» more  GECCO 2004»
15 years 12 months ago
Finding Effective Software Metrics to Classify Maintainability Using a Parallel Genetic Algorithm
The ability to predict the quality of a software object can be viewed as a classification problem, where software metrics are the features and expert quality rankings the class lab...
Rodrigo A. Vivanco, Nicolino J. Pizzi
CEC
2009
IEEE
15 years 11 months ago
A cooperative coevolutionary algorithm with Correlation based Adaptive Variable Partitioning
—A cooperative coevolutionary algorithm (CCEA) is an extension to an evolutionary algorithm (EA); it employs a divide and conquer strategy to solve an optimization problem. In it...
Tapabrata Ray, Xin Yao
GECCO
2008
Springer
117views Optimization» more  GECCO 2008»
15 years 7 months ago
Is "best-so-far" a good algorithmic performance metric?
In evolutionary computation, experimental results are commonly analyzed using an algorithmic performance metric called best-so-far. While best-so-far can be a useful metric, its u...
Nathaniel P. Troutman, Brent E. Eskridge, Dean F. ...