Abstract:
Genetic algorithms have long been used to solve complex optimization problems
by mimicking natural selection processes. However, they often suffer from premature
convergence, reduced diversity, as well as imbalanced exploration and exploitation.
To address these challenges, this work introduces SIS-NGA which integrates
the Susceptible-Infected-Susceptible (SIS) epidemic model and Genetic Algorithms
within a scale-free network topology, to guide the search for optimal solutions. The
SIS model is typically used to capture how infectious diseases spread and evolve
within populations. In this model, individuals in a population are represented as
interconnected nodes in a network. They can transition between two states, namely
susceptible and infected. In analogy, we adapt this formulation to improve the performance
of genetic algorithms. We represent the set of possible solutions to a complex
optimization problem as interconnected nodes in a scale-free network. We assign fit
solutions as infected, with a certain probability. Then, infected nodes can spread their genetic traits to neighboring susceptible nodes through basic genetic algorithm
operations within the SIS framework and based on defined probabilities. The proposed
approach maintains diversity and delays convergence by promoting promising
and optimal solutions. We evaluated SIS-NGA using several benchmark functions,
and our results and statistical analyses confirm consistent improvements in solution
quality and robustness.
