A combination genetic algorithm with applications on portfolio optimization
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Genetic algorithms are often applied to combinatorial optimization problems, the most popular one probably being the traveling salesperson problem. In contrast to permutations used for TSP, the selection of a subset from a larger set has so far gained surprisingly little interest. One intriguing example of this type of problems occurs in diversity selection for virtual high throughput screening, where k molecules need to be selected from a set of n while optimizing certain constraints. In this paper we present a novel representation for k-subsets and several genetic operators for it.