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EC
2000
96views ECommerce» more  EC 2000»
13 years 8 months ago
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied ...
David A. van Veldhuizen, Gary B. Lamont
IANDC
2011
148views more  IANDC 2011»
13 years 3 months ago
Building species trees from larger parts of phylogenomic databases
Abstract. Gene trees are leaf-labeled trees inferred from molecular sequences. Due to duplication events arising in genome evolution, gene trees usually have multiple copies of som...
Celine Scornavacca, Vincent Berry, Vincent Ranwez
BERTINORO
2005
Springer
14 years 1 months ago
Emergent Consensus in Decentralised Systems Using Collaborative Reinforcement Learning
Abstract. This paper describes the application of a decentralised coordination algorithm, called Collaborative Reinforcement Learning (CRL), to two different distributed system pr...
Jim Dowling, Raymond Cunningham, Anthony Harringto...
ISMB
1997
13 years 9 months ago
A Fast Heuristic Algorithm for a Probe Mapping Problem
Anewheuristic algorithm is presented for mapping probes to locations along the genome,given noisy pairwise distance data as input. Themodel consideredis quite general: Theinput co...
Brendan Mumey
GECCO
2006
Springer
179views Optimization» more  GECCO 2006»
13 years 12 months ago
Comparison of multi-objective evolutionary algorithms in optimizing combinations of reinsurance contracts
Our paper concerns optimal combinations of different types of reinsurance contracts. We introduce a novel approach based on the Mean-Variance-Criterion to solve this task. Two sta...
Ingo Oesterreicher, Andreas Mitschele, Frank Schlo...