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GECCO
2009
Springer
110views Optimization» more  GECCO 2009»
15 years 2 months ago
EMO shines a light on the holes of complexity space
Typical domains used in machine learning analyses only partially cover the complexity space, remaining a large proportion of problem difficulties that are not tested. Since the ac...
Núria Macià, Albert Orriols-Puig, Es...
GECCO
2009
Springer
121views Optimization» more  GECCO 2009»
15 years 2 months ago
Using memetic algorithms to improve portfolio performance in static and dynamic trading scenarios
The Portfolio Optimization problem consists of the selection of a group of assets to a long-term fund in order to minimize the risk and maximize the return of the investment. This...
Claus de Castro Aranha, Hitoshi Iba
62
Voted
GECCO
2009
Springer
15 years 2 months ago
Novelty of behaviour as a basis for the neuro-evolution of operant reward learning
An agent that deviates from a usual or previous course of action can be said to display novel or varying behaviour. Novelty of behaviour can be seen as the result of real or appar...
Andrea Soltoggio, Ben Jones
GECCO
2009
Springer
15 years 2 months ago
Problem decomposition using indirect reciprocity in evolved populations
Evolutionary problem decomposition techniques divide a complex problem into simpler subproblems, evolve individuals to produce subcomponents that solve the subproblems, and then a...
Heather Goldsby, Sherri Goings, Jeff Clune, Charle...
GECCO
2009
Springer
159views Optimization» more  GECCO 2009»
15 years 2 months ago
Bayesian network structure learning using cooperative coevolution
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
Olivier Barrière, Evelyne Lutton, Pierre-He...