Three interconnected parameters for genetic algorithms

When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria, selection operator and pressure, and fitness function to be used in order to solve the problem. This paper tests a relationship between (1) crossover probability, (2) mutation probability, and (3) selection pressure using two problems. This relationship is based on the schema theorem proposed by Holland and reflects the fact that the choice of parameters and operators for genetic algorithms needs to be problem specific. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning—parameter learning General Terms Algorithms, Experimentation, Measurement, Performance, Theory, Verification Keywords Performance Analysis, Empirical Study, Evolution Dynamics, Genetic Algorithms, Theory, Working Principles of Evolutionary Computing, Parameter Tuning, Schema Theorem

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Pedro A. Diaz-Gomez, Dean F. Hougen
• Keywords: Artificial Intelligence, GECCO 2009, General Terms Algorithms, Genetic Algorithms, Schema Theorem