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JACM
2006
99views more  JACM 2006»
13 years 8 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller
ACSC
2004
IEEE
13 years 12 months ago
On Evolving Fixed Pattern Strategies for Iterated Prisoner's Dilemma
Table 1 shows the payoff to player one. The same matrix also holds for player two. Player one can gain the maximum 5 points (T = 5) by defection if player two cooperates. However,...
Daniel Jang, Peter A. Whigham, Grant Dick
GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
13 years 12 months ago
Smart crossover operator with multiple parents for a Pittsburgh learning classifier system
This paper proposes a new smart crossover operator for a Pittsburgh Learning Classifier System. This operator, unlike other recent LCS approaches of smart recombination, does not ...
Jaume Bacardit, Natalio Krasnogor
BMCBI
2011
13 years 2 months ago
PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment
Background: The Monte Carlo simulation of sequence evolution is routinely used to assess the performance of phylogenetic inference methods and sequence alignment algorithms. Progr...
Botond Sipos, Tim Massingham, Gregory E. Jordan, N...
GECCO
2007
Springer
184views Optimization» more  GECCO 2007»
14 years 2 months ago
Experimental analysis of binary differential evolution in dynamic environments
Many real-world optimization problems are dynamic in nature. The interest in the Evolutionary Algorithms (EAs) community in applying EA variants to dynamic optimization problems h...
Alp Emre Kanlikilicer, Ali Keles, Sima Uyar