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C. FERREIRA In E. Lutton, J. A. Foster, J. Miller, C. Ryan, and A. G. B. Tettamanzi, eds., Proceedings of the 4th European Conference on Genetic Programming, Lecture Notes in Computer Science, Vol. 2278, pages 51-60, Springer-Verlag, Berlin, Germany, 2002. Discovery of the Boolean Functions to the Best Density-Classification Rules Using Gene Expression Programming Fundamental Classes of Genetic Algorithms   Structurally, genetic algorithms can be subdivided in three fundamental groups: i) Genetic algorithms with individuals consisting of linear chromosomes of fixed length devoid of complex expression. In these systems, replicators (chromosomes) survive by virtue of their own properties. The algorithm invented by Holland [9] belongs to this group, and is known as genetic algorithm or GA; ii) Genetic algorithms with individuals consisting of ramified structures of different sizes and shapes and, therefore, capable of assuming a richer number of functionalities. In these systems, replicators (ramified structures) also survive by virtue of their own properties. The algorithm invented by Cramer [10] and later developed by Koza [1] belongs to this group and is known as genetic programming or GP; iii) Genetic algorithms with individuals encoded as linear chromosomes of fixed length which are afterwards expressed as ramified structures of different sizes and shapes. In these systems, replicators (chromosomes) survive by virtue of causal effects on the phenotype (ramified structures). The algorithm invented by myself [2] belongs to this group and is known as gene expression programming or GEP. The fact that GEP shares with GP the same kind of ramified structure, shows that GEP can be used, for one thing, to retrace easily the steps undertaken by GP and, for another, to explore easily new frontiers opened up by the crossing of the phenotype threshold. Next follows a brief introduction to gene expression programming. Home | Contents | Previous | Next