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» Guided Operators for a Hyper-Heuristic Genetic Algorithm
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CEC
2010
IEEE
12 years 8 months ago
A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment
— Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key...
Jean Berger, Khaled Jabeur, Abdeslem Boukhtouta, A...
GECCO
2005
Springer
117views Optimization» more  GECCO 2005»
13 years 10 months ago
Directional self-learning of genetic algorithm
In order to overcome the low convergence speed and prematurity of classical genetic algorithm, an improved method named directional self-learning of genetic algorithm (DSLGA) is p...
Lin Cong, Yuheng Sha, Licheng Jiao, Fang Liu
PPSN
2000
Springer
13 years 8 months ago
Using Dynastic Exploring Recombination to Promote Diversity in Genetic Search
A family of recombination operators is studied in this work. These operators are based on keeping and using certain information about the past evolution of the algorithm to guide t...
Carlos Cotta, José M. Troya
CEC
2007
IEEE
13 years 11 months ago
A simple genetic algorithm for music generation by means of algorithmic information theory
— Recent large scale experiments have shown that the Normalized Information Distance, an algorithmic information measure, is among the best similarity metrics for melody classiï¬...
Manuel Alfonseca, Manuel Cebrián, Alfonso O...
MICRO
1990
IEEE
147views Hardware» more  MICRO 1990»
13 years 9 months ago
Motivation and framework for using genetic algorithms for microcode compaction
Genetic algorithms are a robust adaptive optimization technique based on a biological paradigm. They perform efficient search on poorly-defined spaces by maintaining an ordered po...
Steven J. Beaty, Darrell Whitley, Gearold Johnson