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Sometimes we tend to forget that:

Whatever the specific approach, competent, scalable genetic algorithm performance can only be obtained when building block linkage information is already in the problem coding or is identified before or during the genetic algorithm’s search. The scalability of genetic algorithms depends critically on the representation. Once the appropriate schemata are identified, the problem becomes “mixing-easy”, and fast, reliable, scalable evolutionary computation can be achieved.

Dirk Thierens, Scalability of Simple Genetic Algorithms, Evolutionary Computation, 331-352, 1999

And we shouldn’t…

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Carlos M. Fernandes was born in Luanda in 1973 and lives in between Lisbon, Portugal, and Granada, Spain. He graduated (Technical University of Lisbon, 1998) in Electrotechnics Engineering and owns a master degree in the same field since 2002 (Technical University of Lisbon). He is currently pursuing a Ph.d. on Bio-inspired Computing. From 2001 to 2005 he was an assistant at Instituto Politécnico de Setúbal. (He is also a photographer and photography teacher.) Bio-inspired Computing is his major field of research: Genetic Algorithms, Estimation of Distribution Algorithms, Ant Colony Optimization, Particle Swarm Optimization and other metaheuristics. He is particularly interested in the hybridization of Bio-inspired Computing techniques with Self-Organization, Self-Organized Criticality Models and diversity maintenance strategies. In the present, Dynamic Optimization Problems are his mains target for applying such techniques. website: www.carlosmfernandes.com email: c.m.fernandes.photo@gmail.com

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