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ATAL
2005
Springer
14 years 3 months ago
Improving reinforcement learning function approximators via neuroevolution
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Shimon Whiteson
GECCO
2005
Springer
140views Optimization» more  GECCO 2005»
14 years 3 months ago
CGP visits the Santa Fe trail: effects of heuristics on GP
GP uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the trees, and GP searches t...
Cezary Z. Janikow, Christopher J. Mann
GECCO
2010
Springer
187views Optimization» more  GECCO 2010»
14 years 2 months ago
The maximum hypervolume set yields near-optimal approximation
In order to allow a comparison of (otherwise incomparable) sets, many evolutionary multiobjective optimizers use indicator functions to guide the search and to evaluate the perfor...
Karl Bringmann, Tobias Friedrich
GECCO
2009
Springer
145views Optimization» more  GECCO 2009»
14 years 2 months ago
Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences
Optimizing the hypervolume indicator within evolutionary multiobjective optimizers has become popular in the last years. Recently, the indicator has been generalized to the weight...
Anne Auger, Johannes Bader, Dimo Brockhoff, Eckart...
EVOW
1994
Springer
14 years 1 months ago
Competitive Evolution: A Natural Approach to Operator Selection
One of the main problems in applying evolutionary optimisation methods is the choice of operators and parameter values. This paper propose a competitive evolution method, in which ...
Q. Tuan Pham