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GECCO
2007
Springer
160views Optimization» more  GECCO 2007»
16 years 1 months ago
Self-modifying cartesian genetic programming
In nature, systems with enormous numbers of components (i.e. cells) are evolved from a relatively small genotype. It has not yet been demonstrated that artificial evolution is su...
Simon Harding, Julian Francis Miller, Wolfgang Ban...
GECCO
2007
Springer
173views Optimization» more  GECCO 2007»
16 years 1 months ago
A hybrid GA for a supply chain production planning problem
The problem of production and delivery lot-sizing and scheduling of set of items in a two-echelon supply chain over a finite planning horizon is addressed in this paper. A single ...
Masoud Jenabi, S. Ali Torabi, S. Afshin Mansouri
GECCO
2007
Springer
276views Optimization» more  GECCO 2007»
16 years 1 months ago
Meta-optimizing semantic evolutionary search
I present MOSES (meta-optimizing semantic evolutionary search), a new probabilistic modeling (estimation of distribution) approach to program evolution. Distributions are not esti...
Moshe Looks
GECCO
2007
Springer
201views Optimization» more  GECCO 2007»
16 years 1 months ago
Evolving explicit opponent models in game playing
Opponent models are necessary in games where the game state is only partially known to the player, since the player must infer the state of the game based on the opponent’s acti...
Alan J. Lockett, Charles L. Chen, Risto Miikkulain...
GECCO
2007
Springer
137views Optimization» more  GECCO 2007»
16 years 1 months ago
Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case
The focus of this paper is on how to design evolutionary algorithms (EAs) for solving stochastic dynamic optimization problems online, i.e. as time goes by. For a proper design, t...
Peter A. N. Bosman, Han La Poutré