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GECCO
2005
Springer
120views Optimization» more  GECCO 2005»
15 years 10 months ago
Exploiting gradient information in numerical multi--objective evolutionary optimization
Various multi–objective evolutionary algorithms (MOEAs) have obtained promising results on various numerical multi– objective optimization problems. The combination with gradi...
Peter A. N. Bosman, Edwin D. de Jong
GECCO
2005
Springer
126views Optimization» more  GECCO 2005»
15 years 10 months ago
Not all linear functions are equally difficult for the compact genetic algorithm
Estimation of distribution algorithms (EDAs) try to solve an optimization problem by finding a probability distribution focussed around its optima. For this purpose they conduct ...
Stefan Droste
GECCO
2005
Springer
146views Optimization» more  GECCO 2005»
15 years 10 months ago
An empirical study of the robustness of two module clustering fitness functions
Two of the attractions of search-based software engineering (SBSE) derive from the nature of the fitness functions used to guide the search. These have proved to be highly robust...
Mark Harman, Stephen Swift, Kiarash Mahdavi
GECCO
2005
Springer
160views Optimization» more  GECCO 2005»
15 years 10 months ago
On the complexity of hierarchical problem solving
Competent Genetic Algorithms can efficiently address problems in which the linkage between variables is limited to a small order k. Problems with higher order dependencies can onl...
Edwin D. de Jong, Richard A. Watson, Dirk Thierens
GECCO
2005
Springer
178views Optimization» more  GECCO 2005»
15 years 10 months ago
A co-evolutionary hybrid algorithm for multi-objective optimization of gene regulatory network models
In this paper, the parameters of a genetic network for rice flowering time control have been estimated using a multiobjective genetic algorithm approach. We have modified the rece...
Praveen Koduru, Sanjoy Das, Stephen Welch, Judith ...