Self-adaptation of Genetic Operators Through Genetic Programming Techniques
This addresses optimization problems for researchers and practitioners in evolutionary computation, but it is incremental as it builds on existing genetic programming methods.
The paper tackles convergence and diversity issues in evolutionary algorithms by proposing a method that self-modifies genetic operators using genetic programming techniques while evolving candidate solutions, leading to better solutions as tested on real benchmark functions.
Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.