A New Approach to Evolutionary Computation: Segregative Genetic Algorithms (SEGA)

This paper looks upon the standard genetic algorithm as an artificial self-organizing process. With the purpose to provide concepts that make the algorithm more open for scalability on the one hand, and that fight premature convergence on the other hand, this paper presents two extensions of the standard genetic algorithm without introducing any problem specific knowledge, as done in many problem specific heuristics on the basis of genetic algorithms. In contrast to contributions in the field of genetic algorithms that introduce new coding standards and operators for certain problems, the introduced approach should be considered as a heuristic appliable to multiple problems of combinatorial optimization, using exactly the same coding standards and operators for crossover and mutation, as done when treating a certain problem with a standard genetic algorithm. The additional aspects introduced within the scope of segregative genetic algorithms (SEGA) are inspired from optimization a...

• Type: Conference
• Year: 2001
• Where: IWANN
• Authors: Michael Affenzeller
• Keywords: Genetic Algorithms, IWANN 2001, Neural Networks, Problem Specific, Standard Genetic Algorithm