Genetic algorithms are a class of optimization and search methods inspired by the principles of natural selection and evolution. They work by maintaining a population of candidate solutions encoded much like chromosomes, then iteratively applying operators analogous to selection, crossover, and mutation so that fitt…
Genetic algorithms are a class of optimization and search methods inspired by the principles of natural selection and evolution. They work by maintaining a population of candidate solutions encoded much like chromosomes, then iteratively applying operators analogous to selection, crossover, and mutation so that fitter solutions are more likely to propagate and improve over successive generations. This evolutionary approach allows genetic algorithms to explore large, complex, and poorly understood solution spaces and to find good solutions to problems where traditional analytical or gradient-based methods are impractical. They are widely applied in engineering design, scheduling, machine learning, and parameter estimation, and are often combined with other computational techniques. In model-based research, genetic algorithms support the development and tuning of models that represent and predict the behavior of complex systems. Related peer-reviewed work in this collection includes a study that couples a genetic algorithm with neural networks to estimate subsurface features of the Earth, demonstrating how evolutionary optimization can be paired with machine-learning models to address difficult inference problems. This page gathers open-access research relevant to evolutionary computation and model-based optimization, supporting study of genetic algorithms and their application to complex problem solving.
2 peer-reviewed articles, ranked by relevance. Each links to its DOI.
The 2 articles above have been cited 5 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.
A sample of recent works citing this journal's research on Genetic Algorithms, linking to each citing work.