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» Evolutionary design search, exploration and optimisation
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EPIA
2007
Springer
14 years 1 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 ...
GECCO
2009
Springer
157views Optimization» more  GECCO 2009»
14 years 2 months ago
Multi material topological optimization of structures and mechanisms
Multi-material 3D-printing technologies permit the freeform fabrication of complex spatial arrangements of materials in arbitrary geometries. This technology has opened the door t...
Jonathan D. Hiller, Hod Lipson
GECCO
2007
Springer
160views Optimization» more  GECCO 2007»
14 years 1 months ago
Hill climbing on discrete HIFF: exploring the role of DNA transposition in long-term artificial evolution
We show how a random mutation hill climber that does multilevel selection utilizes transposition to escape local optima on the discrete Hierarchical-If-And-Only-If (HIFF) problem....
Susan Khor
GECCO
2006
Springer
170views Optimization» more  GECCO 2006»
13 years 11 months ago
How an optimal observer can collapse the search space
Many metaheuristics have difficulty exploring their search space comprehensively. Exploration time and efficiency are highly dependent on the size and the ruggedness of the search...
Christophe Philemotte, Hugues Bersini
BMCBI
2006
85views more  BMCBI 2006»
13 years 7 months ago
Searching for interpretable rules for disease mutations: a simulated annealing bump hunting strategy
Background: Understanding how amino acid substitutions affect protein functions is critical for the study of proteins and their implications in diseases. Although methods have bee...
Rui Jiang, Hua Yang, Fengzhu Sun, Ting Chen