Elements of Evolutionary Algorithms
Evolutionary algorithms are not fixed procedures, but contain several elements that must be adapted to the optimization problem to be solved. In particular, the encoding of the candidate solution needs to be chosen with care. Although there is no generally valid rule or recipe, we discuss some important properties a good encoding should have. We also turn to the fitness function and review the most common selection techniques as well as how certain undesired effects can be avoided by adapting the fitness function or the selection method. The last section of this chapter is devoted to genetic operators, which serve as tools to explore the search space, and covers sexual and asexual recombination and other variation techniques.
KeywordsSearch Space Evolutionary Algorithm Travel Salesman Problem Genetic Operator Good Individual
- Y. Davidor. Lamarckian Sub-Goal Reward in Genetic Algorithm. Proc. Euro. Conf. on Artificial Intelligence (ECAI, Stockholm, Sweden), 189–194. Pitman, London/Boston, United Kingdom/USA, 1990 Google Scholar
- Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs, 3rd (extended) edition. Springer-Verlag, New York, NY, USA, 1996 Google Scholar