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» Efficiently evolving programs through the search for novelty
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GECCO
2010
Springer
158views Optimization» more  GECCO 2010»
9 years 7 months ago
Efficiently evolving programs through the search for novelty
A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to genetic pro...
Joel Lehman, Kenneth O. Stanley
GECCO
2005
Springer
142views Optimization» more  GECCO 2005»
9 years 9 months ago
Toward evolved flight
We present the first hardware-in-the-loop evolutionary optimization on an ornithopter. Our experiments demonstrate the feasibility of evolving flight through genetic algorithms an...
Rusty Hunt, Gregory Hornby, Jason D. Lohn
GPEM
2006
97views more  GPEM 2006»
9 years 4 months ago
Evolving recursive programs by using adaptive grammar based genetic programming
Genetic programming (GP) extends traditional genetic algorithms to automatically induce computer programs. GP has been applied in a wide range of applications such as software ree...
Man Wong
EC
2008
146views ECommerce» more  EC 2008»
9 years 4 months ago
Automated Discovery of Local Search Heuristics for Satisfiability Testing
The development of successful metaheuristic algorithms such as local search for a difficult problems such as satisfiability testing (SAT) is a challenging task. We investigate an ...
Alex S. Fukunaga
AAAI
1994
9 years 5 months ago
Evolving Neural Networks to Focus Minimax Search
Neural networks were evolved through genetic algorithms to focus minimax search in the game of Othello. At each level of the search tree, the focus networks decide which moves are...
David E. Moriarty, Risto Miikkulainen
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