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CEC
2007
IEEE
15 years 7 months ago
SAT-decoding in evolutionary algorithms for discrete constrained optimization problems
— For complex optimization problems, several population-based heuristics like Multi-Objective Evolutionary Algorithms have been developed. These algorithms are aiming to deliver ...
Martin Lukasiewycz, Michael Glaß, Christian ...
96
Voted
EPIA
2007
Springer
15 years 7 months ago
Improving Evolutionary Algorithms with Scouting
The goal of an Evolutionary Algorithm(EA) is to find the optimal solution to a given problem by evolving a set of initial potential solutions. When the problem is multi-modal, an ...
Konstantinos Bousmalis, Gillian M. Hayes, Jeffrey ...
84
Voted
GECCO
2007
Springer
119views Optimization» more  GECCO 2007»
15 years 6 months ago
Optimising the flow of experiments to a robot scientist with multi-objective evolutionary algorithms
A Robot Scientist is a physically implemented system that applies artificial intelligence to autonomously discover new knowledge through cycles of scientific experimentation. Ad...
Emma Byrne
111
Voted
GECCO
2007
Springer
178views Optimization» more  GECCO 2007»
15 years 6 months ago
Transgenetic algorithm: a new evolutionary perspective for heuristics design
Transgenetic algorithms are evolutionary computing techniques based on living processes where cooperation is the main evolutionary strategy. Those processes contain the movement o...
Elizabeth Ferreira Gouvea Goldbarg, Marco Cé...
80
Voted
ISICA
2007
Springer
15 years 6 months ago
A New Evolutionary Decision Theory for Many-Objective Optimization Problems
In this paper the authors point out that the Pareto Optimality is unfair, unreasonable and imperfect for Many-objective Optimization Problems (MOPs) underlying the hypothesis that ...
Zhuo Kang, Lishan Kang, Xiufen Zou, Minzhong Liu, ...