Fitness-based Adaptive Control of Parameters in Genetic Programming: Adaptive Value Setting of Mutation Rate and Flood Mechanisms

TL;DR

This paper introduces adaptive mechanisms for genetic programming that dynamically adjust parameters like mutation rate and flood mechanisms to prevent premature convergence in complex fitness landscapes.

Contribution

It proposes novel adaptive control strategies for mutation and flood mechanisms to enhance genetic programming performance in challenging fitness landscapes.

Findings

Adaptive mechanisms improve search robustness

Prevents premature convergence in complex landscapes

Enhances genetic programming effectiveness

Abstract

This paper concerns applications of genetic algorithms and genetic programming to tasks for which it is difficult to find a representation that does not map to a highly complex and discontinuous fitness landscape. In such cases the standard algorithm is prone to getting trapped in local extremes. The paper proposes several adaptive mechanisms that are useful in preventing the search from getting trapped.