Compact Genetic Algorithm and Iterated Local Search: A Parameter-Less Optimization Framework

Title: Compact Genetic Algorithm and Iterated Local Search: A Parameter-Less Optimization Framework

Research Question: How effective is the proposed parameter-less optimization framework, combining the Compact Genetic Algorithm (ECGA) and Iterated Local Search (ILS), in solving various well-known problems?

Methodology: The researchers presented an optimization algorithm (ILS+ECGA) that combines the Extended Compact Genetic Algorithm (ECGA) and Iterated Local Search (ILS). The algorithm is designed to be parameter-less, eliminating the need for users to set and tune the EA parameters. The effectiveness of the ILS+ECGA algorithm was tested on several well-known problems.

Results: The researchers found that the ILS+ECGA algorithm was a robust and easy-to-use optimization method, demonstrating its effectiveness in solving various problems. The algorithm's performance was compared to other methods, showing that it outperformed them in many cases.

Implications: The parameter-less optimization framework proposed by the researchers can be applied to various types of (selecto-recombinative) GAs, making it a powerful and user-friendly tool for solving optimization problems. The combination of ECGA and ILS in the ILS+ECGA algorithm provides a balanced approach that takes advantage of both population-based and local search strategies, making it suitable for a wide range of problems.

Link to Article: https://arxiv.org/abs/0402047v1 Authors: arXiv ID: 0402047v1