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» On the Brittleness of Evolutionary Algorithms
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GECCO
2007
Springer
168views Optimization» more  GECCO 2007»
16 years 13 days ago
Rigorous analyses of simple diversity mechanisms
It is widely assumed and observed in experiments that the use of diversity mechanisms in evolutionary algorithms may have a great impact on its running time. Up to now there is no...
Tobias Friedrich, Nils Hebbinghaus, Frank Neumann
GECCO
2010
Springer
338views Optimization» more  GECCO 2010»
15 years 4 months ago
Multiobjective evolutionary community detection for dynamic networks
A multiobjective genetic algorithm for detecting communities in dynamic networks, i.e., networks that evolve over time, is proposed. The approach leverages on the concept of evolu...
Francesco Folino, Clara Pizzuti
HIS
2008
15 years 7 months ago
New Crossover Operator for Evolutionary Rule Discovery in XCS
XCS is a learning classifier system that combines a reinforcement learning scheme with evolutionary algorithms to evolve rule sets on-line by means of the interaction with an envi...
Sergio Morales-Ortigosa, Albert Orriols-Puig, Este...
EVOW
2004
Springer
15 years 11 months ago
Improvisational Media Space: Architecture and Strategies for Evolution
This paper presents the current state in an ongoing development of the Emonic Environment (EE): a real-time improvisational system employing evolutionary principles for the mutatio...
Paul Nemirovsky, Rebecca Luger-Guillaume
GECCO
2003
Springer
15 years 11 months ago
Use of Multiobjective Optimization Concepts to Handle Constraints in Single-Objective Optimization
Abstract. In this paper, we propose a new constraint-handling technique for evolutionary algorithms which is based on multiobjective optimization concepts. The approach uses Pareto...
Arturo Hernández Aguirre, Salvador Botello ...