Category: Genetic Algorithms Comparing Performance of the Learnable Evolution Model and Genetic Algorithms Applied to Digital Signal Filters

This paper describes an application of the Learnable Evolution Model (LEM) to a digital signal filter parameter identification problem, and compares its performance with that of two canonical genetic algorithms, GA1 and GA2. The LEM method integrates symbolic learning and evolutionary computation. When the average fitness of a population of solutions has not improved sufficiently during a Darwinian-type evolutionary computational process, LEM generates a hypothesis as to what type of individuals may perform well. This hypothesis, in the form of attributional rules, is then used for generating a new population of solutions. In the experiments on digital signal filter parameter identification LEM significantly outperformed genetic algorithms GA1 and GA2