Understanding Interactions among Genetic Algorithm Parameters

Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex interactions among their parameters. For last two decades, researchers have been trying to understand the mechanics of GA parameter interactions by using various techniques. The methods include careful `functional' decomposition of parameter interactions, empirical studies, and Markov chain analysis. Although the complex knot of these interactions are getting loose with such analyses, it still remains an open question in the mind of a new-comer to the field or to a GA-practitioner as to what values of GA parameters (such as population size, choice of GA operators, operator probabilities, and others) to use in an arbitrary problem. In this paper, we investigate the performance of simple tripartite GAs on a number of simple to complex test problems from a practical standpoint. Since function evaluations are most time-consuming in a real-world problem, we compare different GAs for a fixed...

• Type: Conference
• Year: 1998
• Where: FOGA
• Authors: Kalyanmoy Deb, Samir Agrawal
• Keywords: Crossover Operator, FOGA 1998, FOGA 2007, Operator, Population Size