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A generic evolutionary computation approach based upon genetic algorithms and evolution strategies

Affenzeller M.

Systems Science

2002

Vol. 28, nr 2

59-71

Many problems that are treated by genetic algorithms (GA) belong to the class of NP-complete problems. GA are frequently faced with a problem similar to that of stagnating in a local but not global solution. This drawback, called premature convergence, occurs when the population of a GA reaches such a suboptimal state that the genetic operators can no longer produce offspring that outperforms their parents. The author considers GA as artificial self-organizing processes in a bionically inspired generic way. In doing so he introduces an advanced selection model for GA that allows adaptive selective pressure handling in a way that is quite similar to evolution strategies. This enhanced GA model allows further extensions like the introduction of a concept to handle multiple crossover operators in parallel or the introduction of a concept of segregation and reunification of smaller subpopulations. Both extensions rely on a variable selective pressure. The experimental part of the paper discusses the new algorithms for the traveling salesman problem (TSP) as a well documented instance of a multimodal combinatorial optimization problem achieving results which significantly outperform the results obtained with a contrastable GA.