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» Pareto Optimality in Coevolutionary Learning
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GECCO
2007
Springer
212views Optimization» more  GECCO 2007»
14 years 1 months ago
Controlling overfitting with multi-objective support vector machines
Recently, evolutionary computation has been successfully integrated into statistical learning methods. A Support Vector Machine (SVM) using evolution strategies for its optimizati...
Ingo Mierswa
AAMAS
2007
Springer
13 years 9 months ago
Reaching pareto-optimality in prisoner's dilemma using conditional joint action learning
We consider a repeated Prisoner’s Dilemma game where two independent learning agents play against each other. We assume that the players can observe each others’ action but ar...
Dipyaman Banerjee, Sandip Sen
GECCO
2007
Springer
172views Optimization» more  GECCO 2007»
14 years 3 months ago
Acquiring evolvability through adaptive representations
Adaptive representations allow evolution to explore the space of phenotypes by choosing the most suitable set of genotypic parameters. Although such an approach is believed to be ...
Joseph Reisinger, Risto Miikkulainen
GECCO
2004
Springer
145views Optimization» more  GECCO 2004»
14 years 3 months ago
Automated Extraction of Problem Structure
Most problems studied in artificial intelligence possess some form of structure, but a precise way to define such structure is so far lacking. We investigate how the notion of pr...
Anthony Bucci, Jordan B. Pollack, Edwin D. de Jong
HIS
2007
13 years 11 months ago
Pareto-based Multi-Objective Machine Learning
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Yaochu Jin