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GECCO
2006
Springer
188views Optimization» more  GECCO 2006»
13 years 11 months ago
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
This work describes a forward-looking approach for the solution of dynamic (time-changing) problems using evolutionary algorithms. The main idea of the proposed method is to combi...
Iason Hatzakis, David Wallace
GECCO
2007
Springer
182views Optimization» more  GECCO 2007»
14 years 1 months ago
An analysis of the effects of population structure on scalable multiobjective optimization problems
Multiobjective evolutionary algorithms (MOEA) are an effective tool for solving search and optimization problems containing several incommensurable and possibly conflicting objec...
Michael Kirley, Robert L. Stewart
IJCNN
2006
IEEE
14 years 1 months ago
Generalization Improvement in Multi-Objective Learning
— Several heuristic methods have been suggested for improving the generalization capability in neural network learning, most of which are concerned with a single-objective (SO) l...
Lars Gräning, Yaochu Jin, Bernhard Sendhoff
GECCO
2006
Springer
153views Optimization» more  GECCO 2006»
13 years 11 months ago
Analysis of the difficulty of learning goal-scoring behaviour for robot soccer
Learning goal-scoring behaviour from scratch for simulated robot soccer is considered to be a very difficult problem, and is often achieved by endowing players with an innate set ...
Jeff Riley, Victor Ciesielski
GECCO
2008
Springer
141views Optimization» more  GECCO 2008»
13 years 8 months ago
A study of NK landscapes' basins and local optima networks
We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces [5]. We use the well-known fami...
Gabriela Ochoa, Marco Tomassini, Sébastien ...