UTCS Artificial Intelligence
Eugenic Evolution: The EuA, EuSANE, and TEAM
Active from 1998 - 2002
In standard evolutionary algorithms, new individuals are generated by random mutation and recombination. In Eugenic Evolution, individuals are systematically constructed to maximize fitness, based on historical data on correlations between allele and fitness values. This method, Eugenic Algorithm (EuA), compares favorably to standard methods such as Simulated Annealing and Genetic Algorithms in general combinatorial optimization tasks. The Eugenic principle has also been applied to the evolution of neural networks in a method called EuSANE, where new networks are systematically constructed from a pool of candidate neurons. The EuA principle is further enhanced in the TEAM method, where statistical models for each gene are individually maintained.
mealden [at] uw edu
jprior [at] cs utexas edu
d polani [at] herts ac uk
Aard-Jan van Kesteren
Formerly affiliated Visitor
Eugenic Evolution Utilizing A Domain Model
Matthew Alden, Aard-Jan van Kesteren, and Risto Miikkulainen, In
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002)
, pp. 279-286 2002.
Eugenic Neuro-Evolution For Reinforcement Learning
Daniel Polani and Risto Miikkulainen, In
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
, pp. 1041-1046, San Francisco 2000. Morgan Kaufmann.
Eugenic Evolution For Combinatorial Optimization
John W. Prior, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. 126. Technical Report AI98-268.
The TEAM package contains C++ implementations of both EuA (The Eugenic Algorithm) and TEAM (The Eugenic Algorithm with M...