A Scale-free Network-based Genetic Algorithm with Balanced Exploration and Exploitation

Genetic algorithms (GA) are widely used to solve complex computational problems. They are inspired by natural evolution, and thus belong to the family of evolutionary algorithms. Although GA shows superior performance in many applications, its traditional panmictic version lacks restrictions among individuals, resulting in a lack of communication both intra- and inter-generationally. This paper introduces a novel network-based approach that utilizes phenotypic and genotypic similarities to establish inter-chromosomal links. Based on the scale-free property of the Barabasi-Albert (BA) model, we dynamically assign authority nodes to drive the evolutionary process. Comparative evaluations against panmictic GA and a previously developed network-based genetic algorithm demonstrate favorable results for our suggested approach. The assessments involve the average best fitness over 50 runs conducted on eight well-known benchmark functions. The implementation is freely available at: https://github.com/yazid-hoblos/ENGA.git.