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EMO
2003
Springer
81views Optimization» more  EMO 2003»
15 years 5 months ago
Solving Hierarchical Optimization Problems Using MOEAs
Abstract. In this paper, we propose an approach for solving hierarchical multi-objective optimization problems (MOPs). In realistic MOPs, two main challenges have to be considered:...
Christian Haubelt, Sanaz Mostaghim, Jürgen Te...
NICSO
2010
Springer
15 years 3 months ago
Accelerated Genetic Algorithms with Markov Chains
t] Based on the mutation matrix formalism and past statistics of genetic algorithm, a Markov Chain transition probability matrix is introduced to provide a guided search for comple...
Guan Wang, Chen Chen, Kwok Yip Szeto
GECCO
2007
Springer
150views Optimization» more  GECCO 2007»
15 years 6 months ago
Credit assignment in adaptive memetic algorithms
Adaptive Memetic Algorithms couple an evolutionary algorithm with a number of local search heuristics for improving the evolving solutions. They are part of a broad family of meta...
J. E. Smith
GECCO
2011
Springer
261views Optimization» more  GECCO 2011»
14 years 3 months ago
Spacing memetic algorithms
We introduce the Spacing Memetic Algorithm (SMA), a formal evolutionary model devoted to a systematic control of spacing (distances) among individuals. SMA uses search space dista...
Daniel Cosmin Porumbel, Jin-Kao Hao, Pascale Kuntz
GECCO
2006
Springer
170views Optimization» more  GECCO 2006»
15 years 3 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