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GECCO
2009
Springer
113views Optimization» more  GECCO 2009»
15 years 10 months ago
Environmental robustness in multi-agent teams
Evolution has proven to be an effective method of training heterogeneous multi-agent teams of autonomous agents to explore unknown environments. Autonomous, heterogeneous agents ...
Terence Soule, Robert B. Heckendorn
152
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GECCO
2009
Springer
166views Optimization» more  GECCO 2009»
15 years 10 months ago
Genetic programming in the wild: evolving unrestricted bytecode
We describe a methodology for evolving Java bytecode, enabling the evolution of extant, unrestricted Java programs, or programs in other languages that compile to Java bytecode. B...
Michael Orlov, Moshe Sipper
134
Voted
GECCO
2009
Springer
156views Optimization» more  GECCO 2009»
15 years 10 months ago
Characterizing the genetic programming environment for fifth (GPE5) on a high performance computing cluster
Solving complex, real-world problems with genetic programming (GP) can require extensive computing resources. However, the highly parallel nature of GP facilitates using a large n...
Kenneth Holladay
GECCO
2009
Springer
305views Optimization» more  GECCO 2009»
15 years 10 months ago
Evolving competitive car controllers for racing games with neuroevolution
Modern computer games are at the same time an attractive application domain and an interesting testbed for the evolutionary computation techniques. In this paper we apply NeuroEvo...
Luigi Cardamone, Daniele Loiacono, Pier Luca Lanzi
138
Voted
GECCO
2009
Springer
144views Optimization» more  GECCO 2009»
15 years 10 months ago
Cheating for problem solving: a genetic algorithm with social interactions
We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary...
Rafael Lahoz-Beltra, Gabriela Ochoa, Uwe Aickelin
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